ObjectiveTo investigate the effects of Bushen Huoxue prescription on the cardiac function， inflammation， and quality of life of the patients with chronic heart failure resistant to diuretics. MethodA total of 78 patients who met the inclusion criteria were randomized into observation and control groups （39 cases）. Both groups received standardized treatment for diuretic resistance in accordance with the guidelines. In addition， the observation group received Bushen Huoxue prescription. The cardiac function indicators， total response rate regarding symptom alleviation， exercise endurance， urine volume， body mass， quality of life， and levels of inflammatory cytokines were compared between the two groups before and after treatment. ResultBefore treatment， the two groups of patients showed no significant differences in terms of 24 h urine volume， body mass， 6 minute walk test （6MWT）， Minnesota Living with Heart Failure Questionnaire （MLHFQ） score， N-terminal pro-B-type natriuretic peptide （NT-proBNP） level， left ventricular ejection fraction （LVEF）， stroke volume （SV）， and serum levels of interleukin-6 （IL-6）， interleukin-4 （IL-4）， and tumor necrosis factor-alpha （TNF-α）. After treatment， the observation group outperformed the control group in terms of the response rates regarding traditional Chinese medicine symptom scores and New York Heart Association （NYHA） grading of cardiac function （P<0.05）. After treatment， the body mass， MLHFQ score， and IL-6， TNF-α， and NT-proBNP levels decreased in both groups （P<0.05， P<0.01）， and the observation group showed more significant decreases than the control group （P<0.05， P<0.01）. Both groups showed increases in 24-h urine volume， 6MWT， LVEF， SV， and IL-4 after treatment （P<0.05， P<0.01）， and the observation group showed more significant increases than the control group （P<0.05， P<0.01）. ConclusionThe combination of Bushen Huoxue prescription with standardized treatment is effective in ameliorating the clinical symptoms of the patients with chronic heart failure resistant to diuretics. Moreover， it alleviates diuretic resistance and improves the cardiac function without causing obvious adverse reactions.

Ankylosing spondylitis (AS) combined with lower cervical fracture is often categorized into unstable fracture, with a high incidence of neurological injury and a high rate of disability and morbidity. As factors such as shoulder occlusion may affect the accuracy of X-ray imaging diagnosis, it is often easily misdiagnosed at the primary diagnosis. Non-operative treatment has complications such as bone nonunion and the possibility of secondary neurological damage, while the timing, access and choice of surgical treatment are still controversial. Currently, there are no clinical practice guidelines for the treatment of AS combined with lower cervical fracture with or without dislocation. To this end, the Spinal Trauma Group of Orthopedics Branch of Chinese Medical Doctor Association organized experts to formulate Clinical guidelines for the treatment of ankylosing spondylitis combined with lower cervical fracture in adults ( version 2024) in accordance with the principles of evidence-based medicine, scientificity and practicality, in which 11 recommendations were put forward in terms of the diagnosis, imaging evaluation, typing and treatment, etc, to provide guidance for the diagnosis and treatment of AS combined with lower cervical fracture.

Objective:To explore the value of the Nomogram model established by CT radiomics combined with clinical indicators for prediction of the severity of early acute pancreatitis (AP).Methods:From January 2016 to March 2023, the AP patients in the Second Affiliated Hospital of Soochow University were retrospectively collected. According to the revised Atlanta classification and definition of acute pancreatitis in 2012, all patients were divided into the severe group and the non-severe group. All patients were first diagnosed, and abdominal CT plain scan and enhanced scan were completed within 1 week. Patients were randomly (random number) divided into training and validation groups at a ratio of 7:3. The pancreatic parenchyma was delineated as the region of interest on each phase CT images, and the radiomics features were extracted by python software. LASSO regression and 10-fold cross-validation were used to reduce the dimension and select the optimal features to establish the radiomics signature. Multivariate Logistic regression was used to select the independent predictors of severe acute pancreatitis (SAP), and a clinical model was established. A Nomogram model was established by combining CT radiomics signature and clinical independent predictors. Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the predictive efficacy of each model.Results:Total of 205 AP patients were included (59 cases in severe group, 146 cases in non-severe group). 3, 5, 5 and 5 optimal radiomics features were selected from the plain CT scan, arterial phase, venous phase and delayed phase images of all patients, and the radiomics models were established. Among them, the arterial phase radiomics model had relatively better performance in predicting SAP, with an area under curve (AUC) of 0.937 in the training group and 0.913 in the validation group. Multivariate Logistic regression showed that C-reactive protein (CRP) and lactate dehydrogenase (LDH) were independent predictors of SAP, and they were used to establish a clinical model. The AUC in the training and validation groups were 0.879 and 0.889, respectively. The Nomogram model based on arterial phase CT radiomics signature, CRP and LDH was established, and the AUC was 0.956 and 0.947 in the training group and validation group, respectively. DCA showed that the net benefit of Nomogram model was higher than that of clinical model or radiomics model alone.Conclusions:The Nomogram model established by CT radiomics combined with clinical indicators has high application value for early prediction of the severity of AP, which is conducive to the formulation of clinical treatment plans and prognosis evaluation.

Objective To construct the machine learning models based on the radiomic features of non-contrast and enhanced CT and to evaluate the predictive value in the risk stratification of gastrointestinal stromal tumor(GIST).Methods A total of 182 patients with pathologically confirmed GIST were randomly divided into a training set and a validation set at a ratio of 7∶3.The volume of interest(VOI)was outlined in the non-contrast phase,arterial phase and venous phase,and its radiomic features were extracted.The most valuable radiomic features were selected using the least absolute shrinkage and selection operator(LASSO)algorithm.The logistic regression(LR)classifier was used to construct the prediction models based on single-phase or multi-phase images.The predictive efficacy of the different models was compared by using receiver operating characteristic(ROC)curves.Results Four,three,and four radiomic features were selected in the non-contrast phase,arterial phase and venous phase,and 4 models were constructed in total.Among the single-phase models,the venous phase had better predictive efficacy,with the area under the curve(AUC)of 0.932[95％confidence interval(CI)0.873-0.969]and 0.924(95％CI 0.819-0.979)in the training and validation sets.The predictive efficacy of the combined model was improved,with the AUC of 0.946(95％CI 0.891-0.978)and 0.938(95％CI 0.838-0.986).Conclusion The venous phase model can predict the risk stratification of GIST accurately,and the prediction efficacy can be improved by combining the non-contrast and arterial phases.

Objective:To investigate the early clinical characteristics of elderly patients with severe burns and the risk factors on prognosis.Methods:This study was a retrospective case series study. Clinical data of 124 elderly patients with severe burns who met the inclusion criteria and were admitted to the 12 hospitals from January 2015 to December 2020 were collected, including 4 patients from the Fourth People's Hospital of Dalian, 5 patients from Fujian Medical University Union Hospital, 22 patients from Guangzhou Red Cross Hospital of Jinan University, 5 patients from Heilongjiang Provincial Hospital, 27 patients from the First Affiliated Hospital of Naval Medical University, 9 patients from the First Affiliated Hospital of Nanchang University, 10 patients from Affiliated Hospital of Nantong University, 9 patients from Tongren Hospital of Wuhan University & Wuhan Third Hospital, 12 patients from the 924 th Hospital of PLA, 6 patients from Zhangjiagang First People's Hospital, 4 patients from Taizhou Hospital of Zhejiang Province, and 11 patients from Zhengzhou First People's Hospital. The patients' overall clinical characteristics, such as gender, age, body mass index, total burn area, full-thickness burn area, inhalation injury, causative factors, whether combined with underlying medical diseases, and admission time after injury were recorded. According to the survival outcome within 28 days after injury, the patients were divided into survival group (89 cases) and death group (35 cases). The following data of patients were compared between the two groups, including the basic data and injuries (the same as the overall clinical characteristics ahead); the coagulation indexes within the first 24 hours of injury such as prothrombin time (PT), activated partial thromboplastin time (APTT), thrombin time, D-dimer, fibrinogen degradation product (FDP), international normalized ratio (INR), and fibrinogen; the blood routine indexes within the first 24 hours of injury such as white blood cell count, platelet count, neutrophil-to-lymphocyte ratio, monocyte count, red blood cell count, hemoglobin, and hematocrit; the organ function indexes within the first 24 hours of injury such as direct bilirubin, total bilirubin, urea, serum creatinine, aspartate aminotransferase, alanine aminotransferase, total protein, albumin, globulin, blood glucose, triglyceride, total cholesterol, alkaline phosphatase, creatine kinase, electrolyte indexes (potassium, sodium, chlorine, calcium, magnesium, and phosphorus in blood), uric acid, myoglobin, and brain natriuretic peptide; the infection and blood gas indexes within the first 24 hours of injury such as procalcitonin, C-reactive protein, pH value, oxygenation index, base excess, and lactate; treatment such as whether conducted with mechanical ventilation, whether conducted with continuous renal replacement therapy, whether conducted with anticoagulation therapy, whether applied with vasoactive drugs, and fluid resuscitation. The analysis was conducted to screen the independent risk factors for the mortality within 28 days after injury in elderly patients with severe burns. Results:Among 124 patients, there were 82 males and 42 females, aged 60-97 years, with body mass index of 23.44 (21.09, 25.95) kg/m 2, total burn area of 54.00% (42.00%, 75.00%) total body surface area (TBSA), and full-thickness burn area of 25.00% (10.00%, 40.00%) TBSA. The patients were mainly combined with moderate to severe inhalation injury and caused by flame burns. There were 43 cases with underlying medical diseases. The majority of patients were admitted to the hospital within 8 hours after injury. There were statistically significant differences between patients in the 2 groups in terms of age, total burn area, full-thickness burn area, and inhalation injury, and PT, APTT, D-dimer, FDP, INR, white blood cell count, platelet count, urea, serum creatinine, blood glucose, blood sodium, uric acid, myoglobin, and urine volume within the first 24 hours of injury (with Z values of 2.37, 5.49, 5.26, 5.97, 2.18, 1.95, 2.68, 2.68, 2.51, 2.82, 2.14, 3.40, 5.31, 3.41, 2.35, 3.81, 2.16, and -3.82, respectively, P<0.05); there were statistically significant differences between two groups of patients in whether conducted with mechanical ventilation and whether applied with vasoactive drugs (with χ2 values of 9.44 and 28.50, respectively, P<0.05). Age, total burn area, full-thickness burn area, serum creatinine within the first 24 hours of injury, and APTT within the first 24 hours of injury were the independent risk factors for the mortality within 28 days after injury in elderly patients with severe burns (with odds ratios of 1.17, 1.10, 1.10, 1.09, and 1.27, 95% confidence intervals of 1.03-1.40, 1.04-1.21, 1.05-1.19, 1.05-1.17, and 1.07-1.69, respectively, P<0.05). Conclusions:The elderly patients with severe burns had the injuries mainly from flame burns, often accompanied by moderate to severe inhalation injury and enhanced inflammatory response, elevated blood glucose levels, activated fibrinolysis, and impaired organ function in the early stage, which are associated with their prognosis. Age, total burn area, full-thickness burn area, and serum creatinine and APTT within the first 24 hours of injury are the independent risk factors for death within 28 days after injury in this population.

Objective:To investigate the predictive value of clinical factor model, deep learning model based on baseline plain CT images, and combination of both for predicting hematoma expansion in cerebral hemorrhage.Methods:The study was cross-sectional. Totally 471 cerebral hemorrhage patients who were firstly diagnosed in the Second Affiliated Hospital of Soochow University from January 2017 to December 2021 were collected retrospectively. These patients were randomly divided into a training dataset ( n=330) and a validation dataset ( n=141) at a ratio of 7∶3 by using the random function. All patients underwent two noncontrast CT examinations within 24 h and an increase in hematoma volume of >33% or an absolute increase in hematoma volume of >6 ml was considered hematoma enlargement. According to the presence or absence of hematoma enlargement, all patients were divided into hematoma enlargement group and hematoma non-enlargement group.Two-sample t test, Mann-Whitney U test or χ2 test were used for univariate analysis. The factors with statistically significant differences were included in multivariate logistic regression analysis, and independent influences related to hematoma enlargement were screened out to establish a clinical factor model. ITK-SNAP software was applied to manually label and segment the cerebral hemorrhage lesions on plain CT images to train and build a deep learning model based on ResNet50 architecture. A combination model for predicting hematoma expansion in cerebral hemorrhage was established by combining independent clinical influences with deep learning scores. The value of the clinical factor model, the deep learning model, and the combination model for predicting hematoma expansion in cerebral hemorrhage was evaluated using receiver operating characteristic (ROC) curves and decision curves in the training and validation datasets. Results:Among 471 cerebral hemorrhage patients, 136 cases were in the hematoma enlargement group and 335 cases were in the hematoma non-enlargement group. Regression analyses showed that male ( OR=1.790, 95% CI 1.136-2.819, P=0.012), time of occurrence ( OR=0.812, 95% CI 0.702-0.939, P=0.005), history of oral anticoagulants ( OR=2.157, 95% CI 1.100-4.229, P=0.025), admission Glasgow Coma Scale score ( OR=0.866, 95% CI 0.807-0.929, P<0.001) and red blood cell distribution width ( OR=1.045, 95% CI 1.010-1.081, P=0.011) were the independent factors for predicting hematoma expansion in cerebral hemorrhage. ROC curve analysis showed that in the training dataset, the area under the curve (AUC) of clinical factor model, deep learning model and combination model were 0.688 (95% CI 0.635-0.738), 0.695 (95% CI 0.642-0.744) and 0.747 (95% CI 0.697-0.793) respectively. The AUC of the combination model was better than that of the clinical model ( Z=0.54, P=0.011) and the deep learning model ( Z=2.44, P=0.015). In the validation dataset, the AUC of clinical factor model, deep learning model and combination model were 0.687 (95% CI 0.604-0.763), 0.683 (95% CI 0.599-0.759) and 0.736 (95% CI 0.655-0.806) respectively, with no statistical significance. Decision curves showed that the combination model had the highest net benefit rate and strong clinical practicability. Conclusions:Both the deep learning model and the clinical factor model established in this study have some predictive value for hematoma expansion in cerebral hemorrhage; the combination model established by the two together has the highest predictive value and can be applied to predict hematoma expansion.

Objective:To explore the value of machine learning models based on multiple structural MRI features for diagnosis of Parkinson disease (PD).Methods:The clinical and imaging data of 60 PD patients (PD group) diagnosed in the Neurology Department of the Second Affiliated Hospital of Soochow University from November 2017 to August 2019 and 56 normal elderly people (NC group) recruited from the community were retrospectively analyzed. All subjects underwent brain MR imaging. Multiple structural MRI features were extracted from cerebellum, deep nuclei and of brain cortex based on different partition templates. The Mann-Whitney U test, as well as least absolute shrinkage and selection operator regression were used to select the most discriminating features. Finally, logistic regression (LR) and linear discriminant analysis (LDA) classifier combined with the 5-fold cross-validation scheme were used to construct the models based on structural features of cerebellum, deep nuclei and cortex, and a combined model based on all features. The receiver operating characteristic curves were drawn, and the diagnostic performance and clinical net benefit of each model were evaluated by the area under curve (AUC) and the decision curve analysis (DCA). Results:In total, four cerebellum (asymmetry index of Lobule Ⅵ volume, asymmetry index of Lobule ⅦB cortical thickness, asymmetry index of total gray matter volume and absolute value of right Lobule Ⅵ gray matter volume), 3 deep nuclei (absolute value of right nucleus accumbens volume, absolute and relative value of total nucleus accumbens volume) and 3 cortex features (local gyration index of left PFm, local fractal dimension of right superior frontal gyrus and sulcal depth of left superior occipital gyrus) were selected as the most discriminating features, and the related models were constructed. In validation set, the AUC of cerebellum, deep nuclei, cortex and combined models for diagnosis of PD based on LR classifier were 0.692, 0.641, 0.747 and 0.816; the AUC of cerebellum, deep nuclei, cortex and combined models for diagnosis of PD based on LDA classifier were 0.726, 0.610, 0.752 and 0.818. The diagnostic efficiency of the combined models based on LR and LDA classifiers were significantly better than those of other models ( P<0.05). The DCA curve demonstrated that the combined models based on LR and LDA classifiers showed the highest clinical net benefit. Conclusion:The combined models with all structural features of cerebellum, deep nuclei and cortex included based on LR and LDA classifiers showed favorable performance and clinical net benefit for diagnosis of PD, which have the potential application value in clinical diagnosis.

Objective:To observe the effects of linalool on central and alleviating peripheral nervous system through transient receptor potential vanilloid 1 (TRPV1) pathway and its role in alleviating visceral hypersensitivity in irritable bowel syndrome (IBS).Methods:Thirty-six female newborn Sprague-Dawley (SD) rat pups in specific pothogen free was selected, among which 30 rats were used for behavioral experiments and 6 rats for electrophysiological experiment. In behavioral experiments, 30 newborn SD rats were randomly divided into normal control group (0.2 mL 0.9% sodium chloride solution was injected into the colorectal), neonatal colonic inflammation (NCI) group (0.2 mL 0.5% acetic acid was injected into the colorectal), linalool 20, 50 and 100 mg/kg groups (after NCI model successfully established, the rat were gavaged with linalool 20, 50 or 100 mg/kg at 5 weeks old). At 6 weeks old, abdominal withdrawal reflex (AWR) and pressure value of pain threshold (inflation pressure value at AWR score of 3) was observed by colorectal distention test. One hour after behavioral experiments, the expression of TRPV1 at protein level in colonic mucosa and spinal cord of each group was detected by Western blotting and immunohistochemistry. In electrophysiological experiment, female SD rats aged 6 to 7 weeks were selected to make in vitro transverse sections of the spinal cord. Five to eight neurons were randomly selected from each rat for whole-cell patch-clamp recording. The effects of linalool, tetrodotoxin and anti capsaicinand on the spontaneous excitatory postsynaptic current (sEPSC) of substantiagelatinosa neurons was recorded. Independent sample t test and paired t test were used for statistical analysis. Results:The AWR score of rats in NCI group was higher than those in normal control group, linalool 20 mg/kg group, linalool 50 mg/kg group and linalool 100 mg/kg group at 40 mmHg (1 mmHg=0.133 kPa) pressure (2.5±0.2 vs. 1.0±0.3, 1.5±0.3, 1.5±0.2, and 1.5±0.2, respectively), at 60 mmHg pressure the AWR score was higher than those in normal control group, linalool 50 mg/kg group and linalool 100 mg/kg group (3.8±0.2 vs. 2.3±0.4, 2.3±0.5, and 2.0±0.3, respectively), and the differences were statistically significant ( t=4.39, 2.45, 3.16, 3.16, 3.31, 2.88 and 5.97; P=0.001, 0.034, 0.010, 0.010, 0.008, 0.028, and<0.001). The pain threshold of rats in NCI group was lower than those of normal control group, linalool 20 mg/kg group, linalool 50 mg/kg group and linalool 100 mg/kg group ((35.8±2.0) mmHg vs. (55.8±1.5), (49.2±2.4), (53.3±2.1), and (55.0±1.8) mmHg, respectively), and the differences were statistically significant ( t=-7.91, -4.28, -6.01, and -7.06; P<0.001, =0.002, <0.001, and <0.001). The results of Western blotting showed that the relative expression of TRPV1 at protein level in colorectal tissues of rats in NCI group was higher than those of normal control group, linalool 20 mg/kg group, linalool 50 mg/kg group and linalool 100 mg/kg group (0.86±0.03 vs. 0.32±0.03, 0.68±0.01, 0.45±0.03, and 0.56±0.02, respectively), and the relative expression of TRPV1 at protein level in spinal dorsal horn was higher than those of normal control group, linalool 20 mg/kg group, linalool 50 mg/kg group and linalool 100 mg/kg group (0.91±0.02 vs. 0.34±0.03, 0.72±0.03, 0.51±0.06, and 0.63±0.05), and the differences were statistically significant ( t=12.44, 5.14, 9.68, 7.69, 19.14, 5.13, 6.72, and 5.60; P<0.001, <0.001, <0.001, <0.001, <0.001, <0.001, =0.001, and <0.001). There were no statistically significant differences in the frequency and the outward current of sEPSC in neurons of same glial region among rats with 2 mmol/L linalool gavaged for 4 minutes, 0.5 μmol/L tetrodotoxin and 2 mmol/L linalool gavaged for 4 minutes ( n=5, (23.84±4.81) Hz vs. (20.54±5.71) Hz, (7.60±0.35) pA vs. (7.62±0.75) pA, both P>0.05). The frequency of sEPSC administered with 2 mmol/L linalool gavaged for 4 minutes was higher than that of neurons in the same glial area administered with 10 μmol/L capsazepine and 2 mmol/L linalool gavaged for 4 minutes ( n=5, (20.17±2.55) Hz vs.(14.09±2.98) Hz), and the difference was statistically significant ( t=3.58, P=0.021); however there was no statistically significant difference in the outward current ((7.42±0.78) pA vs. (7.03±1.32) pA, P>0.05). Conclusion:Linalool can relieve visceral hypersensitivity in IBS partially through TRPV1 pathway, which may be related to the hyperpolarization of the membrane potential of neurons in glial region.

Objective:To establish and validate a nomogram model for early prediction of the risk of acute pancreatitis (AP) progressing to severe acute pancreatitis (SAP).Methods:CT signs and clinical laboratory parameters of 361 AP patients admitted to our Hospital from January 2016 to July 2022 were retrospectively collected. There were 221 males (61.2%) and 140 females (38.8%). According to the Atlantic score, all patients were divided into the SAP group (64 cases) and the non-SAP (NSAP) group (297 cases). Univariate analysis was used to screen out variables with statistically significant differences. Multivariate Logistic regression analysis was used to screen out the independent risk factors of SAP, and finally a nomogram prediction model was established. Receiver operating characteristic (ROC) curve, calibration curve and decision curve (DCA) were used to evaluate the predictive efficacy, accuracy and clinical practicability of the model, and Bootstrap method was used to verify the model internally.Results:Univariate analysis and multivariate Logistic regression analysis showed that pleural effusion ( OR=7.353, 95% CI: 3.344-16.170), posterior pararenal space (PPS) involvement ( OR=3.149, 95% CI: 1.314-7.527), serum creatinine concentration (Cr) ( OR=1.027, 95% CI: 1.017-1.038) and serum calcium concentration (Ca 2+) ( OR=0.038, 95% CI: 0.009-0.166) were independent risk factors for SAP ( P<0.05). A Nomogram model was established based on these four factors. The area under the ROC curve (AUC) of this model was 0.905 (95% CI: 0.869-0.933), indicating high predictive efficiency. Internal verification showed that the model had good accuracy in predicting SAP, and C-index was 0.90. DCA analysis showed that the model had high clinical practicability. Conclusions:The Nomogram model combining pleural effusion, PPS involvement, Cr and Ca 2+ had a good effect on early prediction of SAP, which could provide a new reference tool for clinical diagnosis and treatment.

Background and Objectives: Psoriasis is a chronic inflammatory skin disease, which the mechanisms behind its initiation and development are related to many factors. DMSCs (dermal mesenchymal stem cells) represent an important member of the skin microenvironment and play an important role in the surrounding environment and in neighbouring cells, but they are also affected by the microenvironment. We studied the glucose metabolism of DMSCs in psoriasis patients and a control group to reveal the relationship among glucose metabolism, cell proliferation activity，and VEC (vascular endothelial cell) differentiation in vitro, we demonstrated the biological activity and molecular mechanisms of DMSCs in psoriasis. Methods: and Results: We found that the OCR of DMSCs in psoriatic lesions was higher than that in the control group, and mRNA of GLUT1 and HK2 were up-regulated compared with the control group. The proliferative activity of DMSCs in psoriasis was reduced at an early stage, and mRNA involved in proliferation, JUNB and FOS were expressed at lower levels than those in the control group. The number of blood vessels in psoriatic lesions was significantly higher than that in the control group (p＜0.05), which the mRNA of VEC differentiation, CXCL12, CXCR7, HEYL and RGS5 tended to be increased in psoriatic lesions compared to the control group, in addition to Notch3. Conclusions We speculated that DMSCs affected local psoriatic blood vessels through glucose metabolism, and the differentiation of VECs, which resulted in the pathophysiological process of psoriasis.