Objective:To explore the experience of self-management dilemma ofadults with emerging ankylosing spondylitis, and to provide reference for the construction of self-management intervention strategies for emerging adults with ankylosing spondylitis.Methods:Descriptive phenomenology was used to conduct in-depth interviews with 14 adults with emerging ankylosing spondylitis in the Rheumatology and Immunology Department of Drum Tower Hospital Affiliated to Medical College of Nanjing University from August 2022 to March 2023. The interview data were analyzed by Colaizzi′s seven-step analysis method.Results:A total of 14 patients completed the interview,10 males, 4 females, aged 21-30 years. In adults with emerging ankylosing spondylitis, there were dilemmas of role maladjustment and disease management disorder, including role maladjustment of disease management and social role maladjustment. Barriers to disease management included weak self-management awareness, insufficient support for self-management information, inadequate self-management skills, and poor compliance with self-management behaviors.Conclusions:The role adaptation and self-management ability of adults with emerging ankylosing spondylitis are seriously inadequate. It is urgent to construct health management strategies for adults with emerging ankylosing spondylitis to help them improve the level of role adaptation and disease management.

Objective:To summarize the clinical features and prognosis of Budd-Chiari syndrome with hepatopulmonary syndrome (HPS) in children.Methods:The clinical data of a child who had Budd-Chiari syndrome with HPS treated at the Department of Pediatrics of the First Affiliated Hospital of Zhengzhou University in December 2016 was analyzed retrospectively. Taking "Budd-Chiari syndrome" and "hepatopulmonary syndrome" in Chinese or English as the keywords, literature was searched at CNKI, Wanfang, China Biomedical Literature Database and PubMed up to July 2023. Combined with this case, the clinical characteristics, diagnosis, treatment and prognosis of Budd-Chiari syndrome with HPS in children under the age of 18 were summarized.Results:A 13-year-old boy, presented with cyanosis and chest tightness after activities for 6 months, and yellow staining of the skin for 1 week. Physical examination at admission not only found mild yellow staining of the skin and sclera, but also found cyanosis of the lips, periocular skin, and extremities. Laboratory examination showed abnormal liver function with total bilirubin 53 μmol/L, direct bilirubin 14 μmol/L, and indirect bilirubin 39 μmol/L, and abnormal blood gas analysis with the partial pressure of oxygen of 54 mmHg (1 mmHg=0.133 kPa), the partial pressure of carbon dioxide of 31 mmHg, and the alveolar-arterial oxygen gradient of 57 mmHg. Hepatic vein-type Budd-Chiari syndrome, cirrhosis, and portal hypertension were indicated by abdominal CT venography. Contrast-enhanced transthoracic echocardiography (CE-TTE) was positive. After symptomatic and supportive treatment, this patient was discharged and received oxygen therapy outside the hospital. At follow-up until March 2023, there was no significant improvement in hypoxemia, accompanied by limited daily activities. Based on the literature, there were 3 reports in English while none in Chinese, 3 cases were reported. Among a total of 4 children, the chief complaints were dyspnea, cyanosis, or hypoxemia in 3 cases, and unknown in 1 case. There were 2 cases diagnosed with Budd-Chiari syndrome with HPS at the same time due to respiratory symptoms, and 2 cases developed HPS 1.5 years and 8.0 years after the diagnosis of Budd-Chiari syndrome respectively. CE-TTE was positive in 2 cases and pulmonary perfusion imaging was positive in 2 cases. Liver transplantation was performed in 2 cases and their respiratory function recovered well; 1 case received oxygen therapy, with no improvement in hypoxemia; 1 case was waiting for liver transplantation.Conclusions:The onset of Budd-Chiari syndrome with HPS is insidious. The most common clinical manifestations are dyspnea and cyanosis. It can reduce misdiagnosis to confirm intrapulmonary vascular dilatations with CE-TTE at an early stage. Liver transplantation is helpful in improving the prognosis.

Objective:To identify the relationship between the CT arterial radiomics score and the treatment response to neoadjuvant therapy for pancreatic cancer.Methods:The clinical data of 243 pancreatic cancer patients who received surgical resection after neo-adjuvant therapy in the First Affiliated Hospital of Naval Medical University from March 2017 to March 2023 were retrospectively analyzed. Based on the tumor regression grade (TRG), the patients were divided into good response group (TRG 0-1, n=30) and non-good response group (TRG 2-3, n=213). The clinical, radiological and pathological features were compared between two groups. Fully-automated segmentation tool was used for segmenting the arterial CT scan of pancreatic tumor before and after treatment. Python package was applied to extract the radiomics features of tumors after segmentation and the extracted features were reduced and chosen using the least absolute shrinkage and selection operator (Lasso) logistic regression algorithm. Lasso logistic regression formula was applied to calculate the arterial radiomics score. Univariate and multivariate logistic regression models were used to analyze the association between arterial radiomics score and treatment response to neoadjucant therapy. Receiver operating-characteristics (ROC) curve was drawn and area under curve (AUC), specificity, sensitivity and accuracy for evaluating the treatment response were calculated. The clinical usefulness of arterial radiomics score for diagnosing the response of neoadjuvant treatment for pancreatic cancer were determined by decision curve analysis (DCA) . Results:A total of 330 arterial radiomics CT features were obtained, and 9-selected arterial phase features associated with treatment response were determined after being reduced by the Lasso logistic regression algorithm. Univariate analysis showed that the arterial radiomics score, three-dimensional diameter after neoadjuvant therapy, pancreatic contour, T stage, N stage, Peri-pancreatic nerve invasion, lymph-vascular space invasion (LVSI) and invasion of duodenum were all associated with treatment response (all P value <0.05). Multivariate logistic regression analyses confirmed that arterial radiomics score was obviously associated with the neoadjuvant treatment response ( P<0.001). At the cut-off value of 1.93, AUC of the arterial radiomics score for diagnosing neoadjuvant treatment response was 0.92, and the specificity, sensitivity and accuracy was 86.7%, 84.5% and 84.8%. DCA demonstrated that when the percentage for predicting the treatment response by using the arterial radiomics score was >0.2, the patients could benefit from the application of arterial radiomics score for evaluating neoadjuvant therapy response. Conclusions:The arterial radiomics score was strongly correlated with the neoadjuvant treatment response of pancreatic cancer, and can accurately predict neoadjuant treatment efficacy.

Objective:To assess the performance of machine learning(ML),and integrate the clinical parameters with coronary artery calcium(CAC)score of computed tomography(CT)and quantification of automated epicardial adipose tissue(EAT),so as to predict the long-term risk of myocardial infarction(MI)and cardiogenic death in asymptomatic patients.Methods:A total of 1 058 subjects with cardiovascular risk factors and without symptoms of coronary heart disease who underwent physical examination at the Fifth Medical Center of Chinese PLA General Hospital from January 2013 to October 2015 were selected as this study subjects.A long-term follow-up was conducted on them after CAC score.EAT volume and density were quantified using a fully automated deep learning method.ML extreme gradient boosting was trained by using clinical data,risk score of atherosclerotic cardiovascular disease,CAC score and automated EAT measure,and the repeated 10-fold cross validation was used to verify the model.Results:During the 8-year follow-up period,61 cases of 1 058 subjects occurred events of MI and(or)cardiac death.The area under curve(AUC)value of ML was significantly higher than that of the atherosclerotic cardiovascular disease(ASCVD)risk and the predicting events of CAC score(ML:0.82,ASCVD:0.77,CAC:0.77).Compared with ML with only clinical variable,machine learning based on ASCVD,CAC and EAT had more predictive ability for MI and cardiac death[AUC 0.82(95%CI:77-87)vs.0.78(95%CI:0.72-0.84),P=0.02].The survival rate of subjects with high ML scores had a greater decline degree with the increasing of time,therefore,the subjects with higher ML scores were more likely to experience events.Conclusion:ML,which integrated clinical and quantitative imaging variables,can provide long-term risk prediction for patients with cardiovascular risk factors.

Gastric cancer is a common tumor of the gastrointestinal tract, and the global trend in morbidity and mortality are not encouraging. Especially in advanced gastric cancer, patient survival outcome is an essential clinical concern and a vital outcome indicator in clinical outcome assessment. This article reviews the definition of clinical outcome assessment and the measurement tools that can be applied in gastric cancer patients, describes the detailed classification of clinical outcome assessment tools, and reviews the current status of the application of clinical outcome assessment in gastric cancer, analyzing the effects and shortcomings of its application, to provide a reference for the clinical staff in choosing the appropriate tools, and assisting in the comprehensive and holistic assessment of clinical outcomes for the promotion of the development of precision medicine.

Gastric cancer is a common tumor of the gastrointestinal tract, and the global trend in morbidity and mortality are not encouraging. Especially in advanced gastric cancer, patient survival outcome is an essential clinical concern and a vital outcome indicator in clinical outcome assessment. This article reviews the definition of clinical outcome assessment and the measurement tools that can be applied in gastric cancer patients, describes the detailed classification of clinical outcome assessment tools, and reviews the current status of the application of clinical outcome assessment in gastric cancer, analyzing the effects and shortcomings of its application, to provide a reference for the clinical staff in choosing the appropriate tools, and assisting in the comprehensive and holistic assessment of clinical outcomes for the promotion of the development of precision medicine.

With the popularity of imaging examination and the increase in average life expectancy,the detection rate of pancreatic cystic neoplasm(PCN)is also increasing.Different subtypes of PCN have different risks of malignancy,therefore,accurate stratification of the malignant potential is crucial for providing surveillance plans and making surgical decision.In recent years,many guidelines have identified several imaging features(cyst size,mural nodules,and main pancreatic duct diameter)as risk factors for PCN malignancy.However,the measurement standards for individual feature are not uniform and their diagnostic capabilities are limited;models that integrate multiple features perform poorly in diagnosis.This article reviews the value of imaging risk factors in PCN malignancy prediction and the application of radiomics and artificial intelligence,providing direction for further imaging research and improving the accuracy of preoperative PCN risk stratification.

Maxillary sinus floor augmentation using lateral window and crestal technique is considered as predictable methods to increase the residual bone height; however, this surgery is commonly complicated by Schneiderian membrane perforation, which is closely related to anatomical factors. This article aimed to assess anatomical factors on successful augmentation procedures. After review of the current evidence on sinus augmentation techniques, anatomical factors related to the stretching potential of Schneiderian membrane were assessed and a decision tree for the rational choice of surgical approaches was proposed. Schneiderian membrane perforation might occur when local tension exceeds its stretching potential, which is closely related to anatomical variations of the maxillary sinus. Choice of a surgical approach and clinical outcomes are influenced by the stretching potential of Schneiderian membrane. In addition to the residual bone height, clinicians should also consider the stretching potential affected by the membrane health condition, the contours of the maxillary sinus, and the presence of antral septa when evaluating the choice of surgical approaches and clinical outcomes.
Sinus Floor Augmentation ; Decision Trees

Objective:To analyze the expression levels of urinary interleukin-6 (IL-6), signal transducer and activator of transcription 3 (STAT3) and heparin-binding protein (HBP) in urinary tract infection and its correlation with infection prognosis.Methods:The clinical data of 100 patients with urinary tract infection (urinary tract infection group) from January 2021 to December 2022 in the Affiliated Hospital of Jining Medical College were retrospectively analyzed. Among them, simple urinary tract infection was in 62 cases, and complex urinary tract infection was in 38 cases; after treatment, 25 cases were not cured, and 75 cases were cured. Another 50 healthy examinees were selected as the health control group. The level of urine IL-6 was detected by luminescence assay method, the level of urine STAT3 was detected by enzyme-linked immunosorbent assay method, and the level of urine HBP was detected by fluorescence immunochromatography method. The blood routine was detected by fully automated blood cell analyzer, and the blood cell count was recorded. The levels of serum C-reactive protein (CRP) and procalcitonin (PCT) were detected by radioimmunoassay. The correlation between urine IL-6, STAT3, HBP and blood routine inflammatory response markers was analyzed by Pearson method. The receiver operating characteristic (ROC) curve was used to evaluate the predictive effectiveness of urine IL-6, STAT3, HBP and blood routine inflammatory response markers in infection prognosis.Results:The urine IL-6, STAT3, HBP, and blood CRP, PCT, white blood cell count in urinary tract infection group were significantly higher than those in healthy control group: (33.19 ± 11.02) μg/L vs. (16.84 ± 3.57) μg/L, (66.77 ± 19.58) μg/L vs. (38.69 ± 11.04) μg/L, (151.98 ± 42.00) μg/L vs. (28.55 ± 9.16) μg/L, (12.57 ± 4.19) mg/L vs. (5.23 ± 1.80) mg/L, (0.58 ± 0.19) μg/L vs. (0.22 ± 0.07) μg/L and (9.86 ± 3.20) × 10 9/L vs. (6.44 ± 2.13) ×10 9/L, and there were statistical differences ( P<0.01). The urine IL-6, STAT3, HBP, and blood CRP, PCT, white blood cell count in patients with complex urinary tract infection were significantly higher than those in patients with simple urinary tract infection: (40.25 ± 10.34) μg/L vs. (28.87 ± 8.55) μg/L, (79.50 ± 17.92) μg/L vs. (58.96 ± 13.43) μg/L, (186.51 ± 35.92) μg/L vs. (130.82 ± 39.74) μg/L, (14.09 ± 4.18) mg/L vs. (11.64 ± 3.55) mg/L, (0.64 ± 0.20) μg/L vs. (0.55 ± 0.13) μg/L and (11.27 ± 3.08) × 10 9/L vs. (8.99 ± 2.36) × 10 9/L, and there were statistical differences ( P<0.01). The urine IL-6, STAT3, HBP, and blood CRP, PCT, white blood cell count in patients with untreated urinary tract infection were significantly higher than those in patients with cured urinary tract infection: (42.97 ± 11.51) μg/L vs. (29.93 ± 8.66) μg/L, (86.81 ± 20.35) μg/L vs. (60.09 ± 17.43) μg/L, (264.27 ± 28.76) μg/L vs. (114.55 ± 21.38) μg/L, (19.11 ± 3.28) mg/L vs. (10.39 ± 2.40) mg/L, (0.85 ± 0.14) μg/L vs. (0.49 ± 0.11) μg/L and (12.26 ± 2.77) × 10 9/L vs. (9.06 ± 2.34) ×10 9/L, and there were statistical differences ( P<0.01). Pearson correlation analysis result showed that urine IL-6, STAT3, HBP were positively correlated with blood CRP, PCT, white blood cell count ( P<0.01). The ROC curve analysis result showed that the area under curve (AUC) of urine IL-6, STAT3 and HBP in predicting the infection prognosis in patients with urinary tract infection was greater than that of blood CRP, PCT and white blood cell count; moreover, the AUC and sensitivity of the combined of urine IL-6, STAT3 and HBP in predicting the infection prognosis in patients with urinary tract infection were significantly higher than the combined of blood CRP, PCT and white blood cell count (0.937 vs. 0.898 and 96.00% vs. 76.00%), but with lower specificity (81.33% vs. 98.67%). Conclusions:Urinary tract infections can cause elevated urine IL-6, STAT3 and HBP, and the degree of elevation is related to the types of simple or complicated infection and the infection prognosis. The combined detection of the urine IL-6, STAT3 and HBP is expected to be a method to predict the infection prognosis, and it provides reference information for clinical diagnosis and treatment.

Objective:To develop and validate the models based on mixed enhanced computed tomography (CT) radiomics and deep learning features, and evaluate the efficacy for differentiating pancreatic adenosquamous carcinoma (PASC) from pancreatic ductal adenocarcinoma (PDAC) before surgery.Methods:The clinical data of 201 patients with surgically resected and histopathologically confirmed PASC (PASC group) and 332 patients with surgically resected histopathologically confirmed PDAC (PDAC group) who underwent enhanced CT within 1 month before surgery in the First Affiliated Hospital of Naval Medical University from January 2011 to December 2020 were retrospectively collected. The patients were chronologically divided into a training set (treated between January 2011 and January 2018, 156 patients with PASC and 241 patients with PDAC) and a validation set (treated between February 2018 and December 2020, 45 patients with PASC and 91 patients with PDAC) according to the international consensus on the predictive model. The nnU-Net model was used for pancreatic tumor automatic segmentation, the clinical and CT images were evaluated, and radiomics features and deep learning features during portal vein phase were extracted; then the features were dimensionally reduced and screened. Binary logistic analysis was performed to develop the clinical, radiomics and deep learning models in the training set. The models' performances were determined by area under the ROC curve (AUC), sensitivity, specificity, accuracy, and decision curve analysis (DCA).Results:Significant differences were observed in tumor size, ring-enhancement, upstream pancreatic parenchymal atrophy and cystic degeneration of tumor both in PASC and PDAC group in the training and validation set (all P value <0.05). The multivariable logistic regression analysis showed the tumor size, ring-enhancement, dilation of the common bile duct and upstream pancreatic parenchymal atrophy were associated with PASC significantly in the clinical model. The ring-enhancement, dilation of the common bile duct, upstream pancreatic parenchymal atrophy and radiomics score were associated with PASC significantly in the radiomics model. The ring-enhancement, upstream pancreatic parenchymal atrophy and deep learning score were associated with PASC significantly in the deep learning model. The diagnostic efficacy of the deep learning model was highest, and the AUC, sensitivity, specificity, and accuracy of the deep learning model was 0.86 (95% CI 0.82-0.90), 75.00%, 84.23%, and 80.60% and those of clinical and radiomics models were 0.81 (95% CI 0.76-0.85), 62.18%, 85.89%, 76.57% and 0.84 (95% CI 0.80-0.88), 73.08%, 82.16%, 78.59% in the training set. In the validation set, the area AUC, sensitivity, specificity, and accuracy of deep learning model were 0.78 (95% CI 0.67-0.84), 68.89%, 78.02% and 75.00%, those of clinical and radiomics were 0.72 (95% CI 0.63-0.81), 77.78%, 59.34%, 65.44% and 0.75 (95% CI 0.66-0.84), 86.67%, 56.04%, 66.18%. The DCA in the training and validation sets showed that if the threshold probabilities were >0.05 and >0.1, respectively, using the deep learning model to distinguish PASC from PDAC was more beneficial for the patients than the treat-all-patients as having PDAC scheme or the treat-all-patients as having PASC scheme. Conclusions:The deep learning model based on CT automatic image segmentation of pancreatic neoplasm could effectively differentiate PASC from PDAC, and provide a new non-invasive method for confirming PASC before surgery.