Lung transplantation is the optimal treatment for end-stage lung diseases and can significantly improve prognosis of the patients. However, postoperative complications such as infection, rejection, ischemia-reperfusion injury, and other challenges (like shortage of donor lungs) , limit the practical application of lung transplantation in clinical practice. Chinese research teams have been making continuous efforts and have achieved breakthroughs in basic research on lung transplantation by integrating emerging technologies and cutting-edge achievements from interdisciplinary fields, which has strongly propelled the development of this field. This article will comprehensively review the academic progress made by Chinese research teams in the field of lung transplantation in 2024, with a focus on the achievements of Chinese teams in basic research on lung transplantation. It aims to provide innovative ideas and strategies for key issues in the basic field of lung transplantation and to help China's lung transplantation cause reach a higher level.

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.

Background::Coronavirus disease 2019 (COVID-19) has potential risks for both clinically worsening pulmonary hypertension (PH) and increasing mortality. However, the data regarding the protective role of vaccination in this population are still lacking. This study aimed to assess the safety of approved vaccination for patients with PH.Methods::In this national prospective cohort study, patients diagnosed with PH (World Health Organization [WHO] groups 1 and 4) were enrolled from October 2021 to April 2022. The primary outcome was the composite of PH-related major adverse events. We used an inverse probability weighting (IPW) approach to control for possible confounding factors in the baseline characteristics of patients.Results::In total, 706 patients with PH participated in this study (mean age, 40.3 years; mean duration after diagnosis of PH, 8.2 years). All patients received standardized treatment for PH in accordance with guidelines for the diagnosis and treatment of PH in China. Among them, 278 patients did not receive vaccination, whereas 428 patients completed the vaccination series. None of the participants were infected with COVID-19 during our study period. Overall, 398 patients received inactivated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine, whereas 30 received recombinant protein subunit vaccine. After adjusting for baseline covariates using the IPW approach, the odds of any adverse events due to PH in the vaccinated group did not statistically significantly increase (27/428 [6.3%] vs. 24/278 [8.6%], odds ratio = 0.72, P = 0.302). Approximately half of the vaccinated patients reported at least one post-vaccination side effects, most of which were mild, including pain at the injection site (159/428, 37.1%), fever (11/428, 2.6%), and fatigue (26/428, 6.1%). Conclusions::COVID-19 vaccination did not significantly augment the PH-related major adverse events for patients with WHO groups 1 and 4 PH, although there were some tolerable side effects. A large-scale randomized controlled trial is warranted to confirm this finding. The final approval of the COVID-19 vaccination for patients with PH as a public health strategy is promising.

Objective:To establish a predictive model nomogram for 30-day death in patients with sepsis-associated acute kidney injury (SA-AKI) by using the data from the large international database, the Electronic Intensive Care Unit-Collaborative Research Database (eICU-CRD), and to validate its predictive performance.Methods:A retrospective cohort study was conducted using data from the eICU-CRD. Data of SA-AKI patients were screened from the eICU-CRD database, including demographic characteristics, medical history, SA-AKI type, Kidney Disease: Improving Global Outcomes (KDIGO)-AKI staging, severity of illness scores, vital signs, laboratory indicators, and treatment measures; with admission time as the observation start point, death as the outcome event, and a follow-up time of 30 days. Relevant variables of patients with different 30-day prognoses were compared. Univariate Logistic regression analysis and multivariate Logistic regression forward likelihood ratio analysis were used to screen for risk factors associated with 30-day death in SA-AKI patients, and a predictive model nomogram was constructed. Receiver operator characteristic curve (ROC curve), calibration curve, and Hosmer-Lemeshow test were used to validate the predictive performance of the model.Results:A total of 201 SA-AKI patients' data were finally enrolled, among which 51 survived for 30 days and 150 died, with a mortality of 74.63%. Compared with the survival group, patients in the death group were older [years old: 68 (60, 78) vs. 59 (52, 69), P < 0.01], had lower body weight, proportion of transient SA-AKI, platelet count (PLT) and blood glucose [body weight (kg): 79 (65, 95) vs. 91 (71, 127), proportion of transient SA-AKI: 61.33% (92/150) vs. 82.35% (42/51), PLT (×10 9/L): 207 (116, 313) vs. 260 (176, 338), blood glucose (mmol/L): 5.5 (4.4, 7.1) vs. 6.4 (5.1, 7.6), all P < 0.05] and higher proportion of persistent SA-AKI, sequential organ failure assessment (SOFA) score, lactic acid (Lac), and total bilirubin [TBil; proportion of persistent SA-AKI: 38.67% (58/150) vs. 17.65% (9/51), SOFA score: 7 (5, 22) vs. 5 (2, 7), Lac (mmol/L): 0.4 (0.2, 0.7) vs. 0.3 (0.2, 0.4), TBil (μmol/L): 41.0 (17.1, 51.3) vs. 18.8 (17.1, 34.2), all P < 0.05]. Univariate Logistic regression analysis showed that age [odds ratio ( OR) = 1.035, 95% confidence interval (95% CI) was 1.013-1.058, P = 0.002], body weight ( OR = 0.987, 95% CI was 0.977-0.996, P = 0.007), persistent SA-AKI ( OR = 2.942, 95% CI was 1.333-6.491, P = 0.008), SOFA score ( OR = 1.073, 95% CI was 1.020-1.129, P = 0.006), PLT ( OR = 0.998, 95% CI was 0.996-1.000, P = 0.034), Lac ( OR = 1.142, 95% CI was 1.009-1.292, P = 0.035), TBil ( OR = 1.422, 95% CI was 1.070-1.890, P = 0.015) were associated with 30-day death risk in SA-AKI patients. Multivariate Logistic regression forward likelihood ratio analysis showed that age ( OR = 1.051, 95% CI was 1.023-1.079, P = 0.000), body weight ( OR = 0.985, 95% CI was 0.974-0.995, P = 0.005), cardiovascular disease ( OR = 9.055, 95% CI was 1.037-79.084, P = 0.046), persistent SA-AKI ( OR = 3.020, 95% CI was 1.258-7.249, P = 0.013), SOFA score ( OR = 1.076, 95% CI was 1.013-1.143, P = 0.017), and PLT ( OR = 0.997, 95% CI was 0.995-1.000, P = 0.030) were independent risk factors for 30-day death in SA-AKI patients. Based on the above risk factors, a predictive model nomogram for 30-day death in SA-AKI patients was constructed. ROC curve analysis showed that the area under the ROC curve (AUC) of the model was 0.798 (95% CI was 0.722-0.873), with a sensitivity of 86.7% and a specificity of 62.7%. Calibration curve showed that the fitted curve was close to the standard line, indicating that the predicted probability was close to the actual probability, suggesting good predictive performance of the model. Hosmer-Lemeshow test showed χ 2 = 6.393, df = 8, P = 0.603 > 0.05, suggesting that the model could fit the observed data well. The quality of model fitting was judged by the accuracy of model prediction. The results showed that the prediction accuracy rate of the model was 95.3%, and the overall prediction accuracy rate of the model was 81.6%, indicating good model fitting. Conclusion:A predictive model for 30-day death in SA-AKI patients based on risk factors can be successfully constructed, and the model has high accuracy, sensitivity, reliability, and certain specificity, which can help to early identify high-risk patients for death and adopt more proactive treatment strategies.

Objective To analyze the latent profiles of active aging among community older adults and its influencing factors,and explore relationship of different categories and nursing care needs.Methods Multistage stratified random sampling was used to select 341 community older people in Qingdao from June 2023 to August 2023 as the survey population.The study instruments included the General Information Questionnaire,the Active Ageing Scale,the Care Needs of the Elderly Scale.Latent profile analysis was used to explore the latent profiles of active aging on the community older adults.The influencing factors of latent profiles were identified by multivariate Logistic regression.Results The active aging of the community older people was identified as a model with 3 latent categories,defined as low active aging type(38.l％),medium active aging-high spiritual intelligence type type(51.6％),and high active aging-comprehensive type(10.3％).The influencing factors include age,education,monthly income level,nursing needs(all P＜0.05).Community older adults with low self-development and contribution needs are more likely to be low active aging type.Community older adults with high needs for life care and living environment(OR=3.268,P=0.0l 1),physical and psychological support(OR=1.972,P=0.025),and interpersonal communication and health knowledge(OR=3.433,P＜0.001)were more likely to be medium active aging-high spiritual intelligence type.Those with low protection and security needs(OR=0.446,P=0.012)were more likely to be medium active aging-high spiritual intelligence type type.Community older adults with low health monitoring needs(OR=0.297,P=0.029)and high specialty care needs(OR=3.019,P=0.033)were more likely to be high active aging-comprehensive type.Conclusion The level of active aging of older adults in the community is medium,which is characterized by 3 categories.Community nursing staffs should focus on the elderly with low active aging type and medium active aging-high spiritual intelligence type,and targeted intervention should be adopted according to different category characteristics,so as to accurately meet their nursing needs,finally improve the level of active aging of community older adults.

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.

Female ; Humans ; Breast Neoplasms ; Testosterone ; Prolactin ; Estradiol ; Testosterone Congeners ; China

Osteosarcoma is a common primary malignant bone tumor in adolescents and children,characterized by a high recurrence rate and metastasis,making its treatment extremely challenging.Traditional treatment modalities,including surgery,radiation therapy,and chemotherapy,can alleviate symptoms to some extent,but improving long-term survival rates remains a pressing issue.With the continuous development of immunotherapy,breakthroughs have been made in the research of tumor immune microenvironment and the application of immunotherapy in recent years,providing new perspectives and strategies for osteosarcoma treatment.Currently,immunotherapy strategies include tumor vaccines,targeted cytokines,immune checkpoint inhibition,adoptive cell therapy,combination therapy,etc.,significantly enhancing patient immune responses from the aspects of boosting immunity,overcoming immune tolerance,and preventing immune evasion,thereby effectively improving the patients'survival rates and prognosis.This review aims to systematically introduce the immune microenvironment of osteosarcoma and discuss the latest advances in immunotherapy in clinical translational research of osteosarcoma.By deeply understanding the immune characteristics of osteosarcoma and corresponding treatment methods,it is hopeful to provide more effective strategies for personalized treatment,contributing to the improvement of the patients' survival rates and prognosis.

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.

Objective:To investigate the efficacy of camrelizumab combined with second-line therapy in patients with recurrent or metastatic esophageal squamous cell carcinoma (ESCC) in the real-world settings.Methods:Clinical data of 48 patients with esophageal cancer who met the inclusion criteria were retrospectively analyzed. The types of failure after first-line treatment, clinical efficacy, side effects and prognostic factors of second-line treatment were analyzed. SPSS 25.0 software was used for statistical analysis. Count data were expressed by composition ratio and analyzed by Chi-square test or Fisher's exact test. Survival analysis was conducted by Kaplan-Meier curve and log-rank test. Non-normally distributed data were recorded with the median, range and quartile. Results:There were 26, 14, and 4 cases of combined chemoradiotherapy, chemotherapy and radiotherapy in the treatment of second-line camrelizumab, and 4 cases received immunotherapy alone. The median duration of immunotherapy was 6 cycles (range, 2-39 cycles). After second-line treatment, the short-term efficacy of 17, 27 and 4 cases was partial remission (PR), stable disease (SD) and progressive disease (PD), respectively. The overall response rate (ORR) was 35.4% and disease control rate (DCR) was 91.7%. The 1- and 2-year OS rates were 42.9% and 22.5%, and 1- and 2-year PFS rates were 29.0% and 5.8%. The median OS and PFS were 9.0 months (95% CI=6.4-11.7) and 8.5 months (95% CI=1.5-5.6), respectively. Multivariate analysis showed that combined immunotherapy mode, number of cycles of immunotherapy and short-term efficacy were the independent prognostic indicators affecting OS in this group of patients ( HR=2.598, 0.222, 8.330, P=0.044, <0.001, <0.001). Lymphocyte count, neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), combined immunotherapy mode and short-term efficacy were the independent prognostic indicators affecting PFS in this group ( HR=3.704, 3.598, 6.855, 2.159, 2.747, P=0.009, 0.008, <0.001, 0.049, 0.012). Conclusions:Camrelizumab combined with second-line therapy can bring survival benefit to patients with recurrent or metastatic ESCC after first-line therapy, especially immunotherapy combined with chemoradiotherapy can significantly provide survival benefit. Peripheral blood inflammatory biomarkers are independent indicators affecting clinical prognosis of patients. Patients with better short-term efficacy also achieve better prognosis. The final conclusion remains to be validated by a large number of randomized controlled studies.