With the widespread application of low-dose computed tomography (CT), the detection rate of multiple lung cancers (MLCs) is gradually increasing. The diagnosis and treatment of MLCs have become a major challenge in clinical practice in thoracic surgery and oncology. In April 2025, the Lung Cancer Professional Committee of the China Anti-Cancer Association (CACA) organized multidisciplinary experts from both domestic and international fields to release the first edition of the CACA Expert Consensus on the Diagnosis and Treatment of Multiple Lung Cancers, providing systematic recommendations for the diagnostic system, molecularassessment strategies, and surgical and non-surgical management of MLCs. This article provides a detailed interpretation of the core content of this consensus and, by incorporating the latest research progress in the field, delves into the pathogenesis, precise diagnostic strategies, and individualized treatment pathways for multiple lung cancers, aiming to offer a more comprehensive reference for clinical practice.

Chronic heart failure （CHF） is the final stage of cardiovascular diseases. It is a complex syndrome， with dyspnea and edema as the main clinical manifestations， and it is characterized by complex disease conditions， difficult cure， and high mortality. Ferroptosis， a new type of programmed cell death， is different from other types of programmed cell death. Ferroptosis is iron-dependent， accompanied by lipid peroxide accumulation and mitochondrial shrinkage， becoming a hot research topic. Studies have confirmed that ferroptosis plays a key role in the occurrence and development of CHF. The regulation of ferroptosis may become a potential target for the treatment of CHF in the future. The theory of Qi deficiency and stagnation refers to the pathological state of original Qi deficiency and abnormal transportation and distribution of Qi， blood， and body fluid， which has guiding significance for revealing the pathogenesis evolution of some chronic diseases. We believe that Qi deficiency and stagnation is a summary of the pathogenesis of ferroptosis in CHF. Deficiency of Qi （heart Qi） is the root cause of CHF， and stagnation （phlegm turbidity and blood stasis） is the branch of this disease. The two influence each other in a vicious circle to promote the development of this disease. Traditional Chinese medicine （TCM） plays an important role in the treatment of CHF， improving the prognosis and quality of life of CHF patients. This paper explores the correlation between the theory of Qi deficiency and stagnation and the mechanism of ferroptosis in CHF. Furthermore， this paper reviews the mechanism of Chinese medicines and compound prescriptions in preventing and treating CHF by regulating ferroptosis according to the principles of replenishing Qi and dredging to remove stagnation， aiming to provide new ideas and methods for the treatment of CHF with TCM.

Objective To investigate the efficacy of leaflet augmentation technique to repair the recurrent mitral valve (MV) regurgitation after mitral repair in children. Methods A retrospective analysis was conducted on the clinical data of children who underwent redo MV repair for recurrent regurgitation after initial MV repair, using a leaflet augmentation technique combined with a standardized repair strategy at Fuwai Hospital, Chinese Academy of Medical Sciences, from 2018 to 2022. The pathological features of the MV, key intraoperative procedures, and short- to mid-term follow-up outcomes were analyzed. Results A total of 24 patients (12 male, 12 female) were included, with a median age of 37.6 (range, 16.5–120.0) months. The mean interval from the initial surgery was (24.9±17.0) months. All children had severe mitral regurgitation preoperatively. The cardiopulmonary bypass time was (150.1±49.5) min, and the aortic cross-clamp time was (94.0±24.2) min. There were no early postoperative deaths. During a mean follow-up of (20.3±9.1) months, 3 (12.5%) patients developed moderate or severe mitral regurgitation (2 severe, 1 moderate). One (4.2%) patient died during follow-up, and one (4.2%) patient underwent a second MV reoperation. The left ventricular end-diastolic diameter was significantly reduced postoperatively compared to preoperatively [ (43.5±8.6) mm vs. (35.8±7.8)mm, P<0.001]. Conclusion The leaflet augmentation technique combined with a standardized repair strategy can achieve satisfactory short- to mid-term outcomes for the redo mitral repair after previous MV repair. It can be considered a safe and feasible technical option for cases with complex valvular lesions and severe pathological changes.

Shengma Gegentang is one of the classic formulas in the Catalogue of Ancient Classic Prescriptions （Second Batch）. This study reviewed ancient and modern literature and used literature tracing and bibliometric methods to analyze the historical evolution， efficacy， indications， dosage decoctions， and modern clinical disease spectrum of Shengma Gegentang. The results indicated that the earliest record of Shengma Gegentang can be found in the Taiping Huimin Heji Jufang of the Song dynasty， but its origin can be traced back to the Shaoyao Siwu Jiejitang in the Beiji Qianjin Yaofang of the Tang dynasty. The composition dosage of Shengma Gegentang is 413 g of Cimicifugae Rhizoma， 619.5 g of Puerariae Lobatae Radix， 413 g of Paeoniae Radix Alba， and 413 g of Glycyrrhizae Radix et Rhizoma， which are ground into coarse powder. Each dose is 12.39 g， and the amount of water added is 300 mL. 100 mL of solution is decocted and taken at the right time. The four drugs in the formula play the role of relieving exterior syndrome， penetrating pathogenic factors， and detoxicating together. Its indications are widely involved in internal medicine， pediatrics， surgery， ophthalmology and otorhinolaryngology， obstetrics and gynecology， sexually transmitted diseases， and other diseases， such as measles， sores， acne， spots， surgical gangrene， red eyes， toothache， chancre， and fetal poison. The epidemic diseases treated by Shengma Gegentang are complicated， including rash， pox， macula， numbness， summer diarrhea， dysentery， sha disease， febrile symptoms， spring warmth， winter warmth， and cold pestilence. At the same time， it is a plague prevention formula. Although Shengma Gegentang has a wide range of indications， it cannot be separated from the pathogenic mechanism of evil Qi blocking the muscle surface and heat in the lungs and stomach. The modern clinical disease spectrum of Shengma Gegentang involves the ophthalmology and otorhinolaryngology system， nervous system， pediatric-related diseases and syndromes， skin system， hepatobiliary system， and digestive system. It plays a key role in the treatment of epidemic diseases such as measles， chronic hepatitis B， dysentery， and tetanus.

Objective To establish a prenatal prediction scoring formula for evaluating the risk of vaginal trial delivery after cesarean section.The formula will guide the selection of delivery methods.Methods A total of 240 cases of vaginal trial delivery after cesarean section delivered in the Affiliated Women and Children's Hospital of Ningbo University from January 2019 to December 2020 were selected as subjects.According to the final mode of delivery,they were divided into two groups:successful group(n=202)and failed group(n=38).Single factor analysis and multivariate Logistic regression analysis were used to analyze the influencing factors of vaginal delivery after cesarean section.Based on these,prediction scoring formula was established.Results The success rate was 84.2％.The rate of uterine rupture was 0.99％.Overall prediction accuracy of the Logistic regression prediction formula was 83.8％,and fit degree of the formula was good(x2=8.828,P=0.357).The area under the curve was 0.798.Conclusion History of vaginal delivery,thickness of the lower segment of uterus and induced labor are independent influencing factors for vaginal delivery after cesarean section.The prediction scoring formula established by these three factors has good prediction efficiency and high accuracy.

Cervical spinal cord injury without fracture-dislocation (CSCIWFD) is referred to as a special type of cervical spinal cord injury characterized by traumatic spinal cord dysfunction and no significant bony structural abnormalities on imagines. Duo to the high risk of missed diagnosis during the initial consultation, CSCIWFD may lead to progressive neurological deterioration or even complete paralysis, severely impacting patients′ prognosis. Currently, there are no established consensuses over the diagnosis and treatment of CSCIWFD, such as the lack of evidence-based standards for indications of non-surgical treatment and risk of secondary neurological injury, as well as debates over the optimal timing for surgical intervention and indications for different surgical approaches. To address these issues, the Spine Trauma Group of the Orthopedic Branch of the Chinese Medical Doctor Association organized experts in the relevant fields to formulate Diagnosis and treatment guideline for acute cervical spinal cord injury without fracture- dislocation in adults ( version 2025) . Based on evidence-based medicine and the principles of scientific rigor and clinical applicability, the guidelines proposed 11 recommendations covering terminology, diagnosis, evaluation treatment, and rehabilitation, etc., aiming to standardize the management of CSCIWFD.

Antibody humanization is critical to reduce immunogenicity and enhance efficacy in the preclinical phase of the development of therapeutic antibodies originated from animal models.Computational suggestions have long been desired,but available tools focused on immunogenicity calculation of whole antibody sequences and sequence segments,missing the individual residue sites.This study introduces Site-specific Immunogenicity for Therapeutic Antibody(SITA),a novel computational framework that predicts B-cell immunogenicity score for not only the overall antibody,but also individual residues,based on a comprehensive set of amino acid descriptors characterizing physicochemical and spatial features for antibody structures.A transfer-learning-inspired framework was purposely adopted to overcome the scarcity of Antibody-Antibody structural complexes.On an independent testing dataset derived from 13 Antibody-Antibody structural complexes,SITA successfully predicted the epitope sites for Antibody-Antibody structures with a receiver operating characteristic(ROC)-area unver the ROC curve(AUC)of 0.85 and a precision-recall(PR)-AUC of 0.305 at the residue level.Furthermore,the SITA score can significantly distinguish immunogenicity levels of whole human antibodies,therapeutic antibodies and non-human-derived antibodies.More importantly,analysis of an additional 25 thera-peutic antibodies revealed that over 70％of them were detected with decreased immunogenicity after modification compared to their parent variants.Among these,nearly 66％antibodies successfully iden-tified actual modification sites from the top five sites with the highest SITA scores,suggesting the ability of SITA scores for guide the humanization of antibody.Overall,these findings highlight the potential of SITA in optimizing immunogenicity assessments during the process of therapeutic antibody design.

AIM:To investigate the effect of dyes,Remazol BrOrange yellow(RBY)and erythrosine(ERY),on the outcomes of immunoblotting analysis when used for staining the concentrate gel in sodium dodecyl sulfate-polyacryl-amide gel electrophoresis(SDS-PAGE).METHODS:Polyacrylamide gels were divided into five groups:the control group(prepared according to the conventional kit protocol),the RBY-stained group with a final concentration of 0.08 g/L,the RBY-stained group with a final concentration of 0.16 g/L,the ERY-stained group with a final concentration of 0.08 g/L,and the ERY-stained group with a final concentration of 0.8 g/L.Gels were prepared and subjected to electro-phoresis,followed by coomassie brilliant blue staining to visualize protein bands.Subsequently,proteins were transferred to PVDF membranes,which were then blocked,incubated with primary and secondary antibodies,washed,and finally ex-posed for imaging to observe the target protein vinculin bands.RESULTS:Compared with the unstained concentrate gel,the loading wells of the RBY or ERY pre-stained concentrate gel were more clearly visible.Analysis of the gels stained with coomassie brilliant blue after electrophoresis and marker visualization showed no significant different in protein elec-trophoretic mobility between prestained and unstained gels.Comparative analysis of the immunoblotting also indicated that the detection of protein samples transferred to PVDF membranes was unaffected.CONCLUSION:Prestaining concen-trate gels with RBY or ERY can enhance the efficiency of gel-based electrophoresis and immunoblotting analysis.

Objective To explore a deep learning-based automatic bone age estimation model for elbow joint X-ray images of Chinese Han adolescents and children and evaluate its performance.Methods A total of 943(517 males and 426 females)elbow joint frontal view X-ray images of Chinese Han ado-lescents and children aged 6.00 to<16.00 years were collected from East,South,Central and North-west China.Three experimental schemes were adopted for bone age estimation.Scheme 1:Directly in-put preprocessed images into the regression model;Scheme 2:Train a segmentation network using"key elbow joint bone annotations"as labels,then input segmented images into the regression model;Scheme 3:Train a segmentation network using"full elbow joint bone annotations"as labels,then in-put segmented images into the regression model.For segmentation,the optimal model was selected from U-Net,UNet++and TransUNet.For regression,VGG16,VGG19,InceptionV2,InceptionV3,ResNet34,ResNet50,ResNet101 and DenseNet121 models were selected for bone age estimation.The dataset was randomly split into 80%(754 samples)for training and validation for model fitting and hyperparameter tuning,and 20%(189 samples)as an internal test set to test the performance of the trained model.An additional 104 elbow joint X-ray images from the same demographic and age group were col-lected and used as an external test set.Model performance was evaluated by comparing the mean ab-solute error(MAE),root mean square error(RMSE),accuracies within±0.7 years(P±0.7 years)and±1.0 years(P±1.0 years)between the estimated age and the actual age,and by drawing radar charts,scat-ter plots,and heatmaps.Results When segmented with Scheme 3,the UNet++model achieved good segmentation performance with a segmentation loss of 0.000 4 and an accuracy of 93.8%at a learning rate of 0.000 1.In the internal test set,the DenseNet121 model with Scheme 3 yielded the best results with MAE,P±0.7 years and P±1.0 years being 0.83 years,70.03%,and 84.30%,respectively.In the external test set,the DenseNet121 model with Scheme 3 also performed best,with an average MAE of 0.89 years and an average RMSE of 1.00 years.Conclusion When performing automatic bone age estima-tion using elbow joint X-ray images in Chinese Han adolescents and children,it is recommended to use the UNet++model for segmentation.The DenseNet121 model with Scheme 3 achieves optimal per-formance.Using segmentation networks,especially that trained with annotation areas encompassing the full elbow joint including the distal humerus,proximal radius,and proximal ulna,can improve the ac-curacy of bone age estimation based on elbow joint X-ray images.

Objective To explore a deep learning network model suitable for bone age estimation using shoulder joint X-ray images in Chinese Han adolescents.Methods A retrospective collection of 1 286 shoulder joint X-ray images of Chinese Han adolescents aged 12.0 to<18.0 years(708 males and 578 females)was conducted.Using random sampling,approximately 80%of the samples(1 032 cases)were selected as the training and validation sets for model learning,selection and optimization,and the other 20%samples(254 cases)were used as the test set to evaluate the model's generalization ability.The original single-channel shoulder joint X-ray images and dual-channel inputs combining original images with segmentation labels(manually annotated shoulder joint regions multiplied pixel-by-pixel with original images,followed by segmentation via the U-Net++network to retain only key shoulder joint region information)were respectively input into four network models,namely VGG16,ResNet18,ResNet50 and DenseNet121 for bone age estimation.Additionally,manual bone age estimation was con-ducted on the test set data,and the results were compared with the four network models.The mean absolute error(MAE),root mean square error(RMSE),coefficient of determination(R2),and Pear-son correlation coefficient(PCC)were used as main evaluation indicators.Results In the test set,the bone age estimation results of the four models with dual-channel input of shoulder joint X-ray images outperformed those with single-channel input in all four evaluation indicators.Among them,DenseNet121 with dual-channel input achieved best results with MAE of 0.54 years,RMSE of 0.82 years,R2 of 0.76,and PCC(r)of 0.88.Manual estimation yielded an MAE of 0.82 years,ranking second only to dual-channel DenseNet121.Conclusion The DenseNet121 model with dual-channel input combined with original images and segmentation labels is superior to manual evaluation results,and can effectively estimate the bone age of Chinese Han adolescents.