Objective To investigate the diagnositic performance of cardiac myosin-binding protein C (cMyBP-C) in cardiovascular immune-related adverse events (CV-irAE), and to evaluate its potential as a novel biomarker of myocardial injury. Methods Cancer patients receiving immune checkpoint inhibitors (ICI) therapy at Zhongshan Hospital, Fudan University, from January 2025 to September 2025 were enrolled. Patients were divided into the positive group (high-sensitivity cardiac troponin T [hs-cTnT]≥99th percentile [14 pg/mL], n＝78) and the negative group (hs-cTnT＜99th percentile, n＝56). A control group (n＝231) consisted of cancer patients without prior anti-tumor therapy and healthy individuals. Levels of cMyBP-C, hs-cTnT, creatine kinase isoenzyme-MB (CK-MB), and N-terminal pro-brain natriuretic peptide (NT-proBNP) were measured. The screening reference value of cMyBP-C was established using the 99th percentile method. Diagnositic performance was evaluated by receiver operating characteristic (ROC) curves and area under the curve (AUC). The association between cMyBP-C level and myocardial edema assessed by cardiac magnetic resonance (CMR) was analyzed using Spearman correlation. Results The difference in cMyBP-C levels among the three groups was statistically significant (P＜0.001). Based on the 99th percentile of the control group, the screening reference value of cMyBP-C was determined to be 5.86 pg/mL. ROC curves showed that cMyBP-C achieved an AUC of 0.919 (95%CI 0.898–0.939) for detecting the risk of CV-irAE, comparable to CK-MB (AUC＝0.917, 95%CI 0.896–0.939, P＝0.682) and superior to NT-proBNP (AUC＝0.876, 95%CI 0.850–0.902, P＜0.001). After excluding non-cardiovascular adverse events, the AUC of cMyBP-C increased to 0.934, CK-MB was 0.893, and NT-proBNP was 0.877. Spearman correlation analysis revealed that among patients with ICI-associated myocarditis (n＝21), cMyBP-C level was positively correlated with the extent of myocardial edema assessed by CMR ( ρ＝0.64, P＝0.002). Conclusions cMyBP-C is significantly elevated in patients with CV-irAEs, demonstrating diagnositic performance comparable to or better than conventional biomarkers, and reflecting the burden of myocardial inflammation. These findings suggest that cMyBP-C may serve as a valuable complementary diagnositic biomarker for CV-irAEs.

Objective To examine the changing prevalence and antimicrobial resistance profiles of Burkholderia cepacia in 52 hospitals across China from 2015 to 2021.Methods A total of 9 261 strains of B.cepacia were collected from 52 hospitals between January 1,2015 and December 31,2021.Antimicrobial susceptibility of the strains was tested using Kirby-Bauer method or automated antimicrobial susceptibility testing systems according to a unified protocol.The results were interpreted according to the breakpoints released in the Clinical & Laboratory Standards Institute(CLSI)guidelines(2023 edition).Results A total of 9 261 strains of B.cepacia were isolated from all age groups,especially elderly patients.The proportion was 11.1％(1 032 strains)in children,significantly lower than the proportion in adults.About half(46.5％,4 310/9 261)of the strains were isolated from patients at least 60 years old and 42.3％(3 919/9 261)of the strains were isolated from young adults.Most isolates(71.1％)were isolated from sputum and respiratory secretions,followed by urine(10.7％)and blood samples(8.1％).B.cepacia isolates were highly susceptible to the five antimicrobial agents recommended in the CLSI M100 document(33rd edition,2023).B.cepacia isolates showed relatively higher resistance rates to meropenem and levofloxacin.However,the resistance rates to ceftazidime,trimethoprim-sulfamethoxazole,and minocycline remained below 8.1％.The percentage of B.cepacia strains resistant to levofloxacin was the highest compared to other antibiotics in any of the three age groups(from 12.4％in the patients＜18 years old to 20.6％in the patients aged 60 years or older).Conclusions B.cepacia is one of the clinically important non-fermenting gram-negative bacteria.Accurate and timely reporting of antimicrobial susceptibility test results and ongoing antimicrobial resistance surveillance are helpful for rational prescription of antimicrobial agents and proper prevention and control of nosocomial infections.

Objective:To explore the value of 18F-fluorodeoxyglucose ( 18F-FDG) PET-CT dual-modality habitat imaging technology in predicting the epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma. Methods:This study was designed as a cross-sectional study. Clinical and imaging data of 403 patients with lung adenocarcinoma who underwent 18F-FDG PET-CT imaging with definitive EGFR results from January 2018 to April 2022 at the Third Affiliated Hospital of Soochow University were retrospectively analyzed.The patients were divided into a development set (282 cases) and a validation set (121 cases) using a stratified random sampling method at a 7∶3 ratio. An adaptive clustering algorithm was used to segment the regions of interest, forming different habitats and obtaining derived parameters. Independent samples t-test or Mann-Whitney U test were used to compare clinical, imaging indicators, and habitat-derived parameters between EGFR mutant and wild-type patient. The clinical, imaging indicators, and habitat-derived parameters that showed statistically significant differences in univariate analysis were included in multivariate logistic regression to construct clinical and clinical-habitat combined models, respectively. The receiver operating characteristic curve and area under the curve (AUC) were used to evaluate the model′s ability to predict EGFR mutations in lung adenocarcinoma. Additionally, the net reclassification index (NRI) was employed to assess the model′s classification improvement capability. Results:There were 249 cases of EGFR mutation and 154 cases of wild type. The optimal number of habitats was two, namely Habitat 1 and Habitat 2. The parameters included in the clinical model were smoking history, bronchial sign, pleural indentation sign, and tumor diameter. The parameters incorporated into the clinical-habitat combined model were smoking history, bronchial sign, pleural indentation sign, Habitat 2, and Habitat 1 voxel count. In the development set, the AUCs for predicting EGFR mutations in lung adenocarcinoma using the clinical model and the clinical-habitat combined model were 0.723 and 0.733, respectively, with no statistically significant difference ( Z=0.60, P=0.549); In the validation set, the AUCs were 0.684 and 0.715, respectively, with no statistically significant difference ( Z=1.32, P=0.186). The accuracy (0.694) and specificity (0.609) of the clinical-habitat combined model in the validation set were slightly higher than those of the clinical model (0.686 and 0.565, respectively). NRI analysis confirmed that the clinical-habitat combined model improved the correct classification of EGFR wild-type lung adenocarcinoma by 10.9% compared to the clinical model ( P=0.018). Conclusion:18F-FDG PET-CT dual-modality habitat imaging technology can be used to analyze the microenvironment of lung adenocarcinoma and has the potential in non-invasively predicting EGFR mutation status, providing an important basis for personalized and accurate treatment of patients with lung adenocarcinoma.

With the rapid development of generative adversarial networks (GAN), learning the mapping between CT and PET images enables cross-modality generation. This not only integrates anatomical and functional information to improve image quality, but also helps reduce the radiation exposure to some extent. Based on a review of representative GAN architectures such as conditional GAN and CycleGAN, this paper discusses their research progress and limitations in various application scenarios, including initial tumor diagnosis and staging, treatment evaluation and follow-up, as well as methods for reducing PET/CT radiation dose. Additionally, challenges related to small-sample learning, model interpretability, and cross-institutional standardization are highlighted, and the clinical application prospects of GAN-based cross-modality generation technology are explored.

Objective:To develop and assess a three-class machine learning model for predicting wild-type, 19 del, and 21 L858R mutations of the epidermal growth factor receptor (EGFR) in lung adenocarcinoma using 18F-FDG PET/CT radiomic features and clinical features. Methods:The retrospective data was collected from 703 patients (346 males, 357 females; age (64.3±9.0) years) with lung adenocarcinoma at the First People′s Hospital of Changzhou from January 2018 to June 2023. Patients were divided into the training set (563 cases) and test set (140 cases) at the ratio of 8∶2. Clinical features were selected using recursive feature elimination (RFE). Radiomic features were extracted from PET and CT images, and the optimal feature sets were selected using minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) methods. Base models were constructed by using random forest (RF), logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), and multi-layer perceptron (MLP), and the stacking method was applied to establish the CT and PET ensemble models. Delong test was used to compare the AUC differences between the PET/CT combined model and the clinical + PET/CT integrated model.Results:Among 703 patients, 273 were with EGFR wild-type, 202 were with 19 del mutation, and 228 were with 21 L858R mutation. In the single-modal analysis, the AUCs of CT ensemble model in the training and test sets were 0.893 and 0.667, respectively, while the AUCs of PET ensemble model were 0.692 and 0.660. The AUC of PET/CT combined model were 0.897 in training set and 0.672 in test set. The AUC of clinical + PET/CT integrated model showed further improvement, with AUCs of 0.902 and 0.721 in training and test sets, respectively. Notably, the clinical + PET/CT integrated model outperformed PET/CT combined model in predicting wild-type EGFR (test set AUC: 0.784 vs 0.707; Z=3.28, P=0.001). Conclusion:The three-class model (clinical + PET/CT integrated model) based on 18F-FDG PET/CT radiomics and clinical features effectively predicts EGFR mutation subtypes in lung adenocarcinoma.

Objective:To summarize the risk prediction models for postoperative hypoxemia and provide a reference for clinical nursing practice and future research on hypoxemia risk prediction models for postoperative patients.Methods:A systematic literature search was conducted in CNKI, CBM, Wanfang, PubMed, Web of Science Core Collection, Cochrane Library, Embase, and CINAHL databases, covering publications up to January 31, 2024. Two researchers independently screened the literature, extracted data, performed integrative analysis, and evaluated the risk of bias in the included studies.Results:Seventeen studies were included, involving 17 different prediction models. The study populations were primarily adult patients, with hypoxemia incidence rates ranging from 2.40% to 49.30%. Modeling methods included Logistic regression and decision tree algorithms. The presentation formats of the models included risk scoring formulas, nomograms, decision tree diagrams, and web calculators. The five most frequently identified predictive factors were body mass index, age, comorbidities, duration of intraoperative cardiopulmonary bypass, and preoperative white blood cell count. Sixteen models reported the area under the receiver operating characteristic curve ranging from 0.667 to 0.916. All 17 studies exhibited varying degrees of bias risk.Conclusions:Existing risk prediction models for postoperative hypoxemia demonstrate good performance; however, the bias risk level of all studies was high. Future research should standardize the model development process according to bias risk assessment checklists to establish models with low bias risk and strong clinical applicability.

This article provides an overview of ecological momentary assessment based on mobile information devices. It reviews the design frameworks, domestic and international research progress, and the challenges associated with its application. The study aims to offer insights and reference methods for developing electronic ecological momentary assessment platforms in China.

Objective:To develop and assess a three-class machine learning model for predicting wild-type, 19 del, and 21 L858R mutations of the epidermal growth factor receptor (EGFR) in lung adenocarcinoma using 18F-FDG PET/CT radiomic features and clinical features. Methods:The retrospective data was collected from 703 patients (346 males, 357 females; age (64.3±9.0) years) with lung adenocarcinoma at the First People′s Hospital of Changzhou from January 2018 to June 2023. Patients were divided into the training set (563 cases) and test set (140 cases) at the ratio of 8∶2. Clinical features were selected using recursive feature elimination (RFE). Radiomic features were extracted from PET and CT images, and the optimal feature sets were selected using minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) methods. Base models were constructed by using random forest (RF), logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), and multi-layer perceptron (MLP), and the stacking method was applied to establish the CT and PET ensemble models. Delong test was used to compare the AUC differences between the PET/CT combined model and the clinical + PET/CT integrated model.Results:Among 703 patients, 273 were with EGFR wild-type, 202 were with 19 del mutation, and 228 were with 21 L858R mutation. In the single-modal analysis, the AUCs of CT ensemble model in the training and test sets were 0.893 and 0.667, respectively, while the AUCs of PET ensemble model were 0.692 and 0.660. The AUC of PET/CT combined model were 0.897 in training set and 0.672 in test set. The AUC of clinical + PET/CT integrated model showed further improvement, with AUCs of 0.902 and 0.721 in training and test sets, respectively. Notably, the clinical + PET/CT integrated model outperformed PET/CT combined model in predicting wild-type EGFR (test set AUC: 0.784 vs 0.707; Z=3.28, P=0.001). Conclusion:The three-class model (clinical + PET/CT integrated model) based on 18F-FDG PET/CT radiomics and clinical features effectively predicts EGFR mutation subtypes in lung adenocarcinoma.

With the rapid development of generative adversarial networks (GAN), learning the mapping between CT and PET images enables cross-modality generation. This not only integrates anatomical and functional information to improve image quality, but also helps reduce the radiation exposure to some extent. Based on a review of representative GAN architectures such as conditional GAN and CycleGAN, this paper discusses their research progress and limitations in various application scenarios, including initial tumor diagnosis and staging, treatment evaluation and follow-up, as well as methods for reducing PET/CT radiation dose. Additionally, challenges related to small-sample learning, model interpretability, and cross-institutional standardization are highlighted, and the clinical application prospects of GAN-based cross-modality generation technology are explored.

The European Federation of Periodontology (EFP) and the Association for Dental Education in Europe (ADEE) jointly held the second European Consensus Workshop on Education in Periodontology in February, 2023. Building on the first consensus workshop in 2009, expert working groups from European Workshop Committee updated four educational levels: undergraduate, specialist, continuing professional development (CPD), as well as teaching methods, culminating in the updated consensus report in March, 2024. This article introduces and interprets the contents of the 2024 consensus report. Specific to China′s national conditions, we also propose future perspectives and considerations on different levels of periodontal education in China based on this consensus.