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.

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 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.

OBJECTIVE: To investigate the effect and mechanism of Shuangshi Tonglin Capsules (SSTL) in the treatment of prostate fibrosis (PF). METHODS: Human prostate stromal cells (WPMY-1) were used for in vitro experiments to establish PF cell models induced with estradiol (E₂). The cell proliferation, migration and clonogenic capacity were determined by cell counting kit-8, scratch assay, and crystal violet staining, respectively. Sprague-Dawley rats were used for in vivo experiments. The changes in histomorphology and organ index of rat prostate by SSTL were determined. Pathologic changes and collagen deposition changes in rat prostate were observed by haematoxylin and eosin (HE) and Masson staining. Enzyme-linked immunosorbent assay kits were used to determine changes in rat PF markers fibroblast growth factor-23 (FGF-23), E₂ and prostate specific antigen (PSA). Mechanistically, changes in oxidative stress indicators by SSTL were determined in WPMY-1 cells and PF rats. Then the expressions of nuclear factor erythroid 2-related factor 2 (Nrf2)/heme oxygenase-1 (HO-1) and transforming growth factor-β1 (TGF-β1)/Smad pathway-related proteins as well as Nrf2 and TGF-β1 mRNA were further detected by Western blot or quantitative real-time polymerase chain reaction both in vivo and in vitro. RESULTS: In the efficacy study, SSTL significantly reduced the proliferation, migration, and clonogenic ability of cells, improved the morphology of the glandular tissue, significantly reduced the prostate index, reduced glandular fibrous tissue and collagen deposition, and resulted in a significant decrease in the levels of FGF-23, E₂ and PSA (P<0.01 or P<0.05). In the mechanistic study, SSTL ameliorated oxidative stress by significantly increasing superoxide dismutase and glutathione peroxidase levels and decreasing malondialdehyde level in WPMY-1 cells and rats (P<0.01 or P<0.05). SSTL significantly elevated the expressions of Nrf2, HO-1, NAD(P)H quinone oxidoreductase 1 (NQO-1), and Smad7 proteins in both cells and rats, and significantly decreased the expressions of TGF-β1, collagen I, α-smooth muscle actin and Smad4 proteins (P<0.01 or P<0.05). SSTL also elevated the content of Nrf2 mRNA and decreased the content of TGF-β1 mRNA in cells and rats (P<0.01 or P<0.05). The Nrf2 inhibitor ML385 was added in in vitro experiments to further validate the pathway relevance. CONCLUSION SSTL was effective in improving PF in vivo and in vitro, and its mechanism of action may function through the Nrf2/TGF-β1 signaling pathway.
Male ; NF-E2-Related Factor 2/metabolism* ; Animals ; Drugs, Chinese Herbal/therapeutic use* ; Signal Transduction/drug effects* ; Transforming Growth Factor beta1/metabolism* ; Rats, Sprague-Dawley ; Humans ; Fibrosis ; Prostate/drug effects* ; Cell Proliferation/drug effects* ; Capsules ; Cell Movement/drug effects* ; Oxidative Stress/drug effects* ; Rats

Antibody (Ab) humanization is critical to reduce immunogenicity and enhance efficacy in the preclinical phase of the development of therapeutic Abs originated from animal models. Computational suggestions have long been desired, but available tools focused on immunogenicity calculation of whole Ab 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 Ab-Ab structural complexes. On an independent testing dataset derived from 13 Ab-Ab structural complexes, SITA successfully predicted the epitope sites for Ab-Ab 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 Abs, therapeutic Abs and non-human-derived Abs. More importantly, analysis of an additional 25 therapeutic Abs revealed that over 70% of them were detected with decreased immunogenicity after modification compared to their parent variants. Among these, nearly 66% Abs successfully identified 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.

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.

Heat stroke is a heat-related illness caused by an imbalance between the body's heat production and heat dissipation,which could lead to multiple organ dysfunction syndrome with a high mortality rate.Rapid and effective reduction of core body temperature is key to successful treatment.This article reviews recent progress in the treatment of heat stroke,including new understandings of organ injury mechanisms,the timing,velocity and goals of cooling treatment,evaluation and selection of traditional cooling techniques(such as cold water immersion),and scientific evaluation of new cooling technologies(such as blood purification technology and intravascular heat exchange cooling technology),aiming to promote understanding and treatment of heat stroke.

Heart failure （HF） is a group of syndromes caused by cardiac dysfunction with impaired ventricular pumping， seriously affecting patients' health and quality of life. The pathogenesis of HF is complex， including myocardial contractility decline， myocardial fibrosis， and ventricular remodeling， and it is related to neuroendocrine regulation， inflammation， and cardiomyocyte autophagy. Autophagy is a key regulatory mechanism by which cells degrade themselves to maintain body homeostasis. In the process of HF， moderate autophagy can remove aging and damaged cardiomyocytes and maintain the balance of myocardial energy metabolism， while abnormal autophagy may lead to functional decline and pathological changes of cardiomyocytes. The adenosine monophosphate-activated protein kinase （AMPK）/mammalian target of rapamycin （mTOR） signaling pathway is one of the classical pathways regulating autophagy. This pathway can mediate the autophagy of cardiomyocytes and play a role in protecting the cardiac function and delaying HF progression. Traditional Chinese medicine （TCM） with a long history has a unique theoretical system and shows satisfactory therapeutic effects and wide application prospects amid the integration with modern medicine. The clinical practice of TCM has accumulated rich experience in the treatment of cardiovascular diseases. A large number of studies have shown that the active components and compound prescriptions of TCM and Chinese patent medicines can mediate autophagy by regulating the AMPK/mTOR signaling pathway to treat HF. This article explains the role of AMPK/mTOR signaling pathway-mediated autophagy in the treatment of HF， introduces the understanding of autophagy， AMPK/mTOR signaling pathway， and HF based on TCM theories， and reviews the research progress in the regulation of autophagy by TCM in the treatment of HF via the AMPK/mTOR pathway. This review is expected to tap the potential of TCM in the treatment of cardiovascular diseases， provide theoretical support for subsequent experimental studies， and demonstrate the advantages of TCM in clinical practice to achieve more accurate treatment.

Adaptive immunity is a critical component of the human immune system, playing an essential role in identifying antigens and orchestrating a tailored immune response. This review delves into the significant strides made in the development of epitope prediction tools, their integration into vaccine design, and their pivotal role in enhancing immunotherapy strategies. The review emphasizes the transformative potential of these tools in refining our understanding and application of immune responses. Adaptive immunity distinguishes itself from innate immunity by its ability to recognize specific antigens and remember past infections, leading to quicker and more effective responses upon subsequent exposures. This facet of immunity involves complex interactions between various cell types, primarily B cells and T cells, which recognize distinct epitopes presented by antigens. Epitopes are small sequences or configurations on antigens that are recognized by the immune receptors on B cells and T cells, acting as the focal points of immune recognition and response. Epitopes can be broadly classified into two types: linear (or sequential) epitopes and conformational (or discontinuous) epitopes. Linear epitopes consist of a sequence of amino acids in a protein that are recognized by B cells and T cells in their primary structure form. Conformational epitopes, on the other hand, are formed by spatially distinct amino acids that come together in the tertiary structure of the protein, often recognized by the immune system only when the protein folds into its native conformation. The role of epitopes in the immune response is critical as they are the primary triggers for the activation of B cells and T cells. When an epitope is recognized, it can stimulate B cells to produce antibodies, mobilize helper T cells to secrete cytokines, or prompt cytotoxic T cells to kill infected cells. These actions form the basis of the adaptive immune response, tailored to eliminate specific pathogens or infected cells effectively. The prediction of B cell and T cell epitopes has evolved with advances in computational biology, leading to the development of several sophisticated tools that utilize a variety of algorithms to predict the likelihood of epitope regions on antigens. Tools employing machine learning methods, such as support vector machines (SVMs), XGBoost, random forest, analyze large datasets of known epitopes to classify new sequences as potential epitopes based on their similarity to known data. Moreover, deep learning has emerged as a powerful method in epitope prediction, leveraging neural networks capable of learning high-dimensional data from vast amounts of immunological inputs to identify patterns that may not be evident to other predictive models. Deep learning models, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs) and ESM protein language model have demonstrated superior accuracy in mapping the nonlinear relationships inherent in protein structures and epitope interactions. The application of epitope prediction tools in vaccine design is transformative, enabling the development of epitope-based vaccines that can elicit targeted immune responses against specific parts of the pathogen. These vaccines, by focusing the immune response on highly specific regions of the pathogen, can offer high efficacy and reduced side effects. Similarly, in cancer immunotherapy, epitope prediction tools help identify tumor-specific antigens that can be targeted to develop personalized immunotherapeutic strategies, thereby enhancing the precision of cancer treatments. The future of epitope prediction technology appears promising, with ongoing advancements anticipated to enhance the precision and efficiency of these tools further. The integration of broader immunological data, such as patient-specific immune profiles and pathogen variability, along with advances in AI and machine learning, will likely drive the development of more adaptive, robust, and clinically relevant prediction models. This will not only improve the effectiveness of vaccines and immunotherapies but also contribute to our broader understanding of immune mechanisms, potentially leading to breakthroughs in the treatment and prevention of multiple diseases. In conclusion, the development and refinement of epitope prediction tools stand as a cornerstone in the advancement of immunological research and therapeutic design, highlighting a path toward more precise and personalized medicine. The ongoing integration of computational models with experimental immunology holds the promise of revolutionizing our approach to combating infectious diseases and cancer.