Objective To explore a novel method for early lung cancer screening based on exhaled breath analysis. Methods This study enrolled patients with suspected pulmonary malignancies and healthy individuals undergoing physical examinations at Sir Run Run Shaw Hospital, Zhejiang University School of Medicine (Qingchun and Qiantang campuses) from September 2023 to June 2024. Enrolled subjects were categorized into a lung cancer group, a benign nodule/tumor group, and a healthy control group. Exhaled breath samples were collected using a sensor array constructed from multiple graphene composite materials to capture breath fingerprints. Based on the collected data, screening and diagnostic models for lung cancer were developed and their performance was evaluated. Results A total of 4 580 subjects were included. Among them, 3 195 were pathologically diagnosed with pulmonary malignancies, including 1 394 males and 1 801 females with a mean age of (58.93±12.37) years, 599 were diagnosed with benign nodules/tumors including 339 males and 260 females with a mean age of (57.10±11.06) years, and 786 were healthy controls with no pulmonary nodules detected on chest CT including 420 males and 366 females with a mean age of (29.75±9.32) years. There were 4 031 patients in the training set and 549 patients in the external testing set. The screening model for high-risk populations (distinguishing patients with lung cancer/high-risk pulmonary nodules from healthy individuals) demonstrated excellent performance, with an area under the receiver operating characteristic curve (AUC) of 0.926. At the optimal Youden’s index (cutoff threshold of 63.5%), the external testing set achieved a specificity of 85.2%, a sensitivity of 88.4%, and an accuracy of 86.8%. The diagnostic model (distinguishing patients with lung cancer/premalignant lesions from those with benign pulmonary nodules/healthy individuals) achieved an AUC of 0.818. At its optimal Youden’s index (cutoff threshold of 47.0%), the external testing set showed a specificity of 71.7%, a sensitivity of 77.3%, and an accuracy of 74.5%. Conclusion The non-invasive breath analysis platform based on a sensor array, developed in this study, can achieve rapid and relatively accurate lung cancer screening by analyzing breath fingerprints. This confirms the feasibility of this technology for early lung cancer screening and holds promise for facilitating the early detection and intervention of lung cancer.

To thoroughly implement the strategic deployment outlined in the Opinions of the Central Committee of the Communist Party of China and the State Council on Promoting the Inheritance and Innovative Development of Traditional Chinese Medicine regarding research on dominant diseases of traditional Chinese medicine and to uphold the development philosophy of equal emphasis on traditional Chinese medicine and western medicine，the China Association of Chinese Medicine has fully played a leading academic role by systematically organizing and conducting a series of academic youth salons on clinical dominant diseases of traditional Chinese medicine. On September 13，2024，the 36^th Youth Salon on Clinical Dominant Diseases was successfully held in Nanjing，focusing on the advantages of traditional Chinese medicine and the integrative traditional Chinese medicine and western medicine in the diagnosis and treatment of erectile dysfunction （ED）. The conference brought together leading experts from traditional Chinese medicine，western medicine，and interdisciplinary fields，facilitating in-depth multidisciplinary discussions that led to key consensus on optimizing traditional Chinese medicine treatment protocols for ED，researching and developing new drugs of traditional Chinese medicine，and advancing interdisciplinary development in traditional Chinese medicine. This salon systematically sorted out the clinical strengths and distinctive features of traditional Chinese medicine in the diagnosis and treatment of ED. Based on current research foundations and clinical needs，it identified key directions for future scientific layout and scientific research tackling： （1） Standardization of syndrome differentiation system of traditional Chinese medicine for ED. （2） Optimization and standardization of intervention methods of integrated traditional Chinese medicine and western medicine. （3） High-quality clinical research guided by evidence-based medicine. （4） In-depth analysis of the pharmacological mechanisms of traditional Chinese medicine in the treatment of ED. （5） Clinical translation and application promotion of new drugs of traditional Chinese medicine. （6） Interdisciplinary integration and innovation in traditional Chinese medicine. For each research direction，key focus areas，expected objectives，and clinical value were further refined，along with the establishment of a scientifically sound priority funding level evaluation system. Therefore，building on the series of salons on the ED-focused dominant diseases of traditional Chinese medicine，this paper provides standardized guidance for clinical practice of traditional Chinese medicine in ED management，effectively contributing to the high-quality development of traditional Chinese medicine. It serves as a valuable reference for national scientific and technological strategic layout， research and development decision-making in new drugs of traditional Chinese medicine，research topic planning，and clinical guideline formulation.

Pulmonary hypertension(PH)is a fatal disease characterized by pulmonary vascular remodeling,ultimately leading to right heart failure and death.Current treatments for PH are suboptimal,with no substantial improvement in overall survival among patients with advanced PH.Despite some progress in understanding the pathogenesis of PH,further studies at the molecular level are needed to develop more effective treatments for PH.Recent research has shown that long non-coding RNAs(lncRNAs)have an important regulatory function in the pathophysiological process of PH,and may thus be potential disease biomarkers and therapeutic targets.In this paper,we review recent progress in our understanding of the molecular mechanisms of lncRNAs in PH.

Objective To investigate the CT and MRI features of giant cell reparative granuloma(GCRG)in lateral skull base.Methods The CT,MRI and clinicopathological data of 8 patients with GCRG in lateral skull base were collected and analyzed.Results All of the eight lesions were unilateral and solitary(three on the left side and five on the right side),with widespread involvement affecting two or more bony structures of the lateral skull base.All lesions showed expansile and lytic bone destruction on CT scans,the margins were clear(7/8),and the sclerotic changes could be seen at the margin of all eight lesions.On MRI,the lesions revealed heterogeneous isointense and hypointense on T1WI,and heterogeneous hypointense with focal cystic changes on T2WI,without fluid-fluid levels.The enhanced scan showed heterogeneous enhancement.Seven cases extended to the middle cranial fossa,caused com-pression of the temporal lobe brain tissue,with thickened and strengthened adjacent meninges.Conclusion The GCRG in lateral skull base has certain characteristic appearances on CT and MRI;understanding these characteristic manifestations can provide a basis for accurate diagnosis.

Objective To analyze risk factors for poor postoperative wound healing in spinal tuberculo-sis patients and construct prediction models along with a risk scoring table using machine learning algorithms,providing references for early prevention and management.Methods Clinical data from 420 spinal tuberculo-sis patients treated at four tertiary hospitals in Guizhou Province between January 2017 and February 2024 were retrospectively analyzed.Risk factors were identified through univariate and multivariate analyses.Logis-tic regression,random forest,and support vector machine prediction models were constructed.Model perform-ance was evaluated using receiver operating characteristic(ROC)curves,precision,recall,accuracy,and F1-score.A risk prediction scoring table for poor postoperative wound healing was subsequently developed.Re-sults Among the 420 patients with spinal tuberculosis,132 experienced poor postoperative wound healing,with an incidence rate of 31.43%.Logistic regression analysis showed that BMI≤18.5 kg/m2,preoperative albumin≤30 g/L,combined surgical procedure,intraoperative blood loss>1 000 mL,direct bilirubin>8 μmol/L within 3 days after surgery,neutrophil count≤75×109/L within 3 days after surgery,and postopera-tive drainage volume>500 mL were risk factors for poor postoperative wound healing(P<0.05).In the comparison among the three risk prediction models constructed based on machine learning algorithms,the ran-dom forest model demonstrated the best predictive performance.A risk prediction scoring table was construc-ted based on the partial regression coefficients from the multivariate analysis,with a total score range from 0 to 11 points.A score>6 points indicated an increased risk of poor postoperative wound healing.The area un-der the ROC curve(AUC)for this risk prediction scoring table was 0.846(95%CI:0.769 to 0.923),indica-ting good predictive performance.Conclusion The random forest model based on machine learning algorithms and the risk prediction scoring table have certain predictive value for assessing the risk of poor postoperative wound healing in patients with spinal tuberculosis;they can assist healthcare professionals in early identifica-tion of high-risk patients.

Objective:To investigate the association of serum γ-glutamyltransferase (GGT) with stroke severity and outcome in patients with acute ischemic stroke (AIS).Methods:Patients with AIS admitted to the Department of Neurology, Hefei Second People's Hospital (Guangde Road Branch) from January 2023 to December 2023 were included retrospectively. According to the baseline National Institutes of Health Stroke Scale score, the patients were divided into mild stroke group (≤8) and moderate to severe stroke group (>8); According to the modified Rankin Scale score at 3 months after onset, the patients were divided into a good outcome group (0-2) and a poor outcome group (>2). Multivariate logistic regression analysis was used to determine the independent influencing factors of stroke severity and outcome, and the receiver operating characteristic (ROC) curve was used to evaluate the predictive value of serum GGT for poor outcome. Results:A total of 136 patients with AIS were included, with 42 patients (30.88%) in the mild stroke group and 94 (69.12%) in the moderate to severe stroke group; 80 patients (58.82%) in the good outcome group and 56 (41.18%) in the poor outcome group. Multivariate logistic regression analysis showed that higher serum GGT was an independent related factor for moderate to severe stroke (odds ratio [ OR] 1.075, 95% confidence interval [ CI] 1.017-1.135; P<0.05) and poor outcome ( OR 1.131, 95% CI 1.069-1.197; P<0.05). The ROC curve analysis showed that the area under the curve for predicting poor outcome by serum GGT was 0.820 (95% CI 0.747-0.892). Conclusion:Serum GGT is significantly correlated with the severity of stroke in patients with AIS, and has certain predictive value for poor short-term outcome.

Pulmonary hypertension (PH) is a group of progressive diseases characterized by pulmonary vascular remodeling,and some patients already have right heart insufficiency at the time of diagnosis. Therefore,early diagnosis of PH is essential to improve patients' quality of life and prolong survival. Biomarkers are an important indicator for early diagnosis of the disease,and there are many traditional biomarkers for PH diagnosis,but the sensitivity and specificity are low. With the progress of research,some new biomarkers have been shown to predict disease progression in early PH and play an important role in the early diagnosis of PH. This study reviews the research progress of biomarkers of PH from the aspects of right heart insufficiency,endothelial dysfunction,pulmonary artery smooth muscle dysfunction,inflammation,and in situ thrombosis to provide exploration direction and reference value for early diagnosis of PH.

Reporting guidelines are structured checklists for researchers to follow when reporting spe-cific types of studies.As researches conducted in real-world settings to address practical issues,implementa-tion research has stringent requirements for the replicability of result and the transparency of reporting,making its reporting guidelines particularly important.This paper systematically introduces the reporting guidelines in the field of implementation science,outlines their classification systems and scopes of applica-tion,and focuses on explaining the core characteristics and functions of five key reporting guidelines,inclu-ding the Standards for Reporting Implementation Studies(StaRI),Reporting guidelines for implementation and operational research,the Template for Intervention Description and Replication(TIDieR),the Frame-work for Reporting Adaptations and Modifications-Enhanced(FRAME),and recommendations for specifying and reporting implementation strategies.Furthermore,combined with the PEDALs research paradigm in im-plementation science,this paper further clarifies the specific application pathways for reporting guidelines and discusses directions for refinement,aiming to provide references for researchers to select appropriate reporting guidelines.

BACKGROUND: Based on the China-VHD database, this study sought to develop and validate a Valvular Heart Disease- specific Age-adjusted Comorbidity Index (VHD-ACI) for predicting mortality risk in patients with VHD. METHODS & RESULTS: The China-VHD study was a nationwide, multi-centre multi-centre cohort study enrolling 13,917 patients with moderate or severe VHD across 46 medical centres in China between April-June 2018. After excluding cases with missing key variables, 11,459 patients were retained for final analysis. The primary endpoint was 2-year all-cause mortality, with 941 deaths (10.0%) observed during follow-up. The VHD-ACI was derived after identifying 13 independent mortality predictors: cardiomyopathy, myocardial infarction, chronic obstructive pulmonary disease, pulmonary artery hypertension, low body weight, anaemia, hypoalbuminaemia, renal insufficiency, moderate/severe hepatic dysfunction, heart failure, cancer, NYHA functional class and age. The index exhibited good discrimination (AUC, 0.79) and calibration (Brier score, 0.062) in the total cohort, outperforming both EuroSCORE II and ACCI (P < 0.001 for comparison). Internal validation through 100 bootstrap iterations yielded a C statistic of 0.694 (95% CI: 0.665-0.723) for 2-year mortality prediction. VHD-ACI scores, as a continuous variable (VHD-ACI score: adjusted HR (95% CI): 1.263 (1.245-1.282), P < 0.001) or categorized using thresholds determined by the Yoden index (VHD-ACI ≥ 9 vs. < 9, adjusted HR (95% CI): 6.216 (5.378-7.184), P < 0.001), were independently associated with mortality. The prognostic performance remained consistent across all VHD subtypes (aortic stenosis, aortic regurgitation, mitral stenosis, mitral regurgitation, tricuspid valve disease, mixed aortic/mitral valve disease and multiple VHD), and clinical subgroups stratified by therapeutic strategy, LVEF status (preserved vs. reduced), disease severity and etiology. CONCLUSION The VHD-ACI is a simple 13-comorbidity algorithm for the prediction of mortality in VHD patients and providing a simple and rapid tool for risk stratification.