This paper summarizes Professor WANG Xixing's clinical experience in treating immune checkpoint inhibitor-related pneumonitis （CIP） based on the theory of "cough attributed to the five zang （脏） organs". Cough is a common predominant symptom of CIP. According to the theory of "cough attributed to the five zang organs"， drug toxicity triggers cancer toxin， leading to disharmony among the five zang organs， and then lung failing to diffuse and govern descent as the core pathogenesis. Therefore， treatment should focus on harmonizing the five zang organs to restore the normal function of lung qi to diffuse and govern descent. In clinical practice， CIP can be classified into four syndrome patterns， including lung yin depletion， deficiency of both the lung and the spleen with phlegm-dampness， liver fire harassing the lung， and lung-kidney yin deficiency. Correspondingly， Chaimai Jinluo Runfei Decoction （柴麦金络润肺汤） is used to nourish yin and moisten the lung； Qigui Peitu Huayin Decoction （芪桂培土化饮汤） is used to fortify the spleen and tonify the lung， resolve dampness and dispel phlegm； Chaidan Shuyu Runjin Decoction （柴丹疏郁润金汤） is used to drain liver and clear the lung； and Dimai Jinshui Xiangsheng Decoction （地脉金水相生汤） is used to nourish the kidney and moisten the lung.

Traditional Chinese medicine (TCM) demonstrates distinctive advantages in disease prevention and treatment. However, analyzing its biological mechanisms through the modern medical research paradigm of "single drug, single target" presents significant challenges due to its holistic approach. Network pharmacology and its core theory of network targets connect drugs and diseases from a holistic and systematic perspective based on biological networks, overcoming the limitations of reductionist research models and showing considerable value in TCM research. Recent integration of network target computational and experimental methods with artificial intelligence (AI) and multi-modal multi-omics technologies has substantially enhanced network pharmacology methodology. The advancement in computational and experimental techniques provides complementary support for network target theory in decoding TCM principles. This review, centered on network targets, examines the progress of network target methods combined with AI in predicting disease molecular mechanisms and drug-target relationships, alongside the application of multi-modal multi-omics technologies in analyzing TCM formulae, syndromes, and toxicity. Looking forward, network target theory is expected to incorporate emerging technologies while developing novel approaches aligned with its unique characteristics, potentially leading to significant breakthroughs in TCM research and advancing scientific understanding and innovation in TCM.
Artificial Intelligence ; Medicine, Chinese Traditional ; Humans ; Network Pharmacology/methods* ; Drugs, Chinese Herbal/pharmacology* ; Animals ; Multiomics

The activation proteins released by fibroblasts in the tumor microenvironment regulate tumor growth, migration, and treatment response, thereby influencing tumor progression and therapeutic outcomes. Owing to the proliferation and metastasis of tumors, fibroblast activation protein (FAP) is typically highly expressed in the tumor stroma, whereas it is nearly absent in adult normal tissues and benign lesions, making it an attractive target for precision medicine. Radiolabeled agents targeting FAP have the potential for targeted cancer diagnosis and therapy. This comprehensive review aims to describe the evolution of FAPI-based radiopharmaceuticals and their structural optimization. Within its scope, this review summarizes the advances in the use of radiolabeled small molecule inhibitors for tumor imaging and therapy as well as the modification strategies for FAPIs, combined with insights from structure-activity relationships and clinical studies, providing a valuable perspective for radiopharmaceutical clinical development and application.

Drug development remains a critical issue in the field of biomedicine. With the rapid advancement of information technologies such as artificial intelligence (AI) and the advent of the big data era, AI-assisted drug development has become a new trend, particularly in predicting drug-target associations. To address the challenge of drug-target prediction, AI-driven models have emerged as powerful tools, offering innovative solutions by effectively extracting features from complex biological data, accurately modeling molecular interactions, and precisely predicting potential drug-target outcomes. Traditional machine learning (ML), network-based, and advanced deep learning architectures such as convolutional neural networks (CNNs), graph convolutional networks (GCNs), and transformers play a pivotal role. This review systematically compiles and evaluates AI algorithms for drug- and drug combination-target predictions, highlighting their theoretical frameworks, strengths, and limitations. CNNs effectively identify spatial patterns and molecular features critical for drug-target interactions. GCNs provide deep insights into molecular interactions via relational data, whereas transformers increase prediction accuracy by capturing complex dependencies within biological sequences. Network-based models offer a systematic perspective by integrating diverse data sources, and traditional ML efficiently handles large datasets to improve overall predictive accuracy. Collectively, these AI-driven methods are transforming drug-target predictions and advancing the development of personalized therapy. This review summarizes the application of AI in drug development, particularly in drug-target prediction, and offers recommendations on models and algorithms for researchers engaged in biomedical research. It also provides typical cases to better illustrate how AI can further accelerate development in the fields of biomedicine and drug discovery.

Objective To investigate the prevalence and the risk factors of heart disease(HD)in adults aged ≥80 years old in China based on the data from the 8th round of the Chinese Longitudinal Healthy Longevity Survey(CLHLS).Methods A total of 7 675 adults aged ≥80 years old were enrolled from the 8th round of CLHLS dataset.Chi-square tests were employed to examine associations between cardiovascular disease and demographic characteristics,socioeconomic status,social support,lifestyle factors,and health indicators.Logistic regression models were developed to analyze significant predictors of heart disease in the elderly.Results The prevalence of heart disease was 16％(n=1 228)in 7 675 elderly people.Aged 90-99 years old(odds ratio[OR]=0.816),≥100 years old(OR=0.641),female(OR=0.833),and low body mass index(BMI)(＜18.5 kg/m2,OR=0.778)were the protective factors for cardiovascular disease in the elderly;and high BMI(24.0 to 27.9 kg/m2,OR=1.209),rural residence(OR=2.384),health examination(OR=1.164),dysfunction of daily living activities(OR=1.401),hypertension(OR=2.143),diabetes mellitus(OR=1.719),and history of cerebrovascular accident(OR=2.080)were risk factors.Conclusion Male,overweight,rural residence,health examination,dysfunction of daily living activities,hypertension,diabetes mellitus,and a history of cerebrovascular accident are the risk factors for heart disease in the elderly.

Objective:To investigate the efficacy of self-prescribed Huoxue Shengjing Prescription as adjuvant therapy for varicocele-induced infertility and its impact on the outcomes of in vitro fertilization-embryo transfer/intracytoplasmic sperm injection (IVF-ET/ICSI).Methods:A randomized controlled trial was conducted. A total of 99 patients with varicocele-induced infertility in our hospital from January 2022 to July 2023 were selected as observation subjects and divided into three groups using a random number table method, with 33 patients in each group. The low ligation group received low ligation of varicocele under a microscope, the low ligation + conventional Western medicine treatment group received low ligation + conventional Western medicine therapy, and the combined group received low ligation + conventional Western medicine therapy + a self-prescribed Huoxue Shengjing Prescription. Among them, the low ligation of varicocele under a microscope was followed by IVF-ET/ICSI assisted reproductive technology 3 months after surgery; the conventional Western medicine therapy involved continuous administration of L-carnitine oral solution for 3 months; the self-prescribed Huoxue Shengjing Prescription was started on the first day after surgery and continued for 3 months. TCM syndrome scores were assessed before and after treatment, and semen routine analysis was performed using an automated semen quality analyzer. Mitochondrial activity of granulosa cells was measured using the Hrudka extraction method, and sperm nuclear DNA integrity was assessed using a modified alkaline single-cell gel electrophoresis method. Follow-up was conducted for 1 year to observe and record the outcomes of IVF-ET/ICSI and evaluate the clinical efficacy.Results:The total effective rate was 93.9% (31/33) in the combined group, 69.7% (23/33) in the low ligation group, and 75.8% (25/33) in the low ligation + conventional Western medicine treatment group, with statistical significance ( χ 2=6.52, P=0.039). After treatment, the scores for mild abdominal pain, testicular heavy pain, impotence, mental fatigue, and the total score in the combined group were lower than those in the low ligation + conventional Western medicine treatment group and the low ligation group ( F values were 89.29, 97.51, 136.36, 155.06, and 311.13, respectively, P<0.001). The sperm survival rate, sperm concentration, normal morphology rate, and progressive motility rate in the combined group were higher than those in the low ligation + conventional Western medicine treatment group and the low ligation group ( F values were 19.23, 11.85, 35.97, and 52.21, respectively, P<0.001). Mitochondrial grade I cell activity of granulosa cells was higher than that of the low ligation + conventional treatment group and low ligation group ( F=23.23, P<0.001), and grade Ⅲ cell activity was lower than that of the low ligation + conventional treatment group and low ligation group ( F=20.28, P<0.001). After treatment, the detection of grade Ⅰ and Ⅱ sperm nuclear DNA integrity in the combined group were higher than those in the low ligation + conventional Western medicine treatment group and the low ligation group ( F values were 17.73 and 18.39, respectively, P<0.001), while grades Ⅲ and Ⅳ were lower than those in the low ligation + conventional Western medicine treatment group and the low ligation group ( F values were 29.07 and 10.36, respectively, P<0.001). During follow-up, the excellent embryo rate and the spouse's clinical pregnancy rate in the combined group were higher than those in the low ligation + conventional Western medicine treatment group and the low ligation group ( χ2 values were 14.92 and 8.38, respectively; P values were 0.001 and 0.015, respectively). Conclusion:The adjuvant treatment with a self-prescribed Huoxue Shengjing Prescription can enhance sperm quality in patients with varicocele-related infertility, maintain DNA integrity, regulate seminal plasma mitochondrial function, increase the rate of high-quality embryos, and improve the spouse's pregnancy outcomes.

Objective:To elucidate the expression of angiopoietin-like protein 4 (ANGPTL4) in LPS-induced septic cardiomyopathy tissue and cardiomyocyte, and to explore the mechanism of ANGPTL4 in septic cardiomyopathy.Methods:Fifty C57BL/6 mice, aged 8 weeks, were randomly(random number) divided into a treatment group (LPS) and a control group ( n = 25 each). The mice in the treatment group were intraperitoneally injected with LPS (10 mg/kg) to establish a sepsis model. After 24 h, the myocardial tissues of the mice in the sepsis group and the control group, which were caused by LPS, were collected for RNA sequencing to pick out the differentially expressed gene of ANGPTL4.Ventricular myocytes of neonatal mice were taken, and the silencing and overexpression vectors of ANGPTL4 were transfected. After 48 hours of transfection, the cells were collected for subsequent detection. Western blot method was used to detect the expression of apoptotic factors Bax, Bcl-2, and Caspase 3 in mouse ventricular myocytes; CCK8 method was used to detect the activity of ventricular myocytes; using the Annexin V-FITC and PI double staining method, the apoptosis of ventricular myocytes was detected. Results:RNA-seq analysis revealed a statistically significant upregulation of ANGPTL4 expression at both transcriptional and translational levels in the ventricular tissue of septic mice, as compared to the control group ( P<0.05). The results of qRT-PCR and Western blot indicated that the mRNA and protein levels of ANGPTL4 in the ventricular tissues and cardiomyocytes of mice treated with LPS were significantly increased ( P<0.05). After transfection of the silencing and overexpression vectors of ANGPTL4 in cardiomyocytes, it was found that compared with NC, the mRNA and protein expression levels of ANGPTL4 in the si-ANGPTL4 group significantly decreased ( P<0.05), the vitality of ventricular myocytes increased ( P<0.05), the expressions of apoptosis-related factors Bax and Caspase 3 significantly decreased ( P<0.05), and the expression of Bcl-2 significantly increased ( P<0.05), and the number of apoptotic cells significantly decreased ( P<0.05); while the transfection of the overexpression vector of ANGPTL4 showed an opposite trend. Conclusions:In septic myocardial tissue and cardiomyocyte, the expression of ANGPTL4 is elevated, resulting in the inhibition of ventricular myocyte viability and the promotion of cardiomyocyte apoptosis.

Drug development remains a critical issue in the field of biomedicine.With the rapid advancement of information technologies such as artificial intelligence(AI)and the advent of the big data era,AI-assisted drug development has become a new trend,particularly in predicting drug-target associations.To address the challenge of drug-target prediction,AI-driven models have emerged as powerful tools,of-fering innovative solutions by effectively extracting features from complex biological data,accurately modeling molecular interactions,and precisely predicting potential drug-target outcomes.Traditional machine learning(ML),network-based,and advanced deep learning architectures such as convolutional neural networks(CNNs),graph convolutional networks(GCNs),and transformers play a pivotal role.This review systematically compiles and evaluates AI algorithms for drug-and drug combination-target predictions,highlighting their theoretical frameworks,strengths,and limitations.CNNs effectively identify spatial patterns and molecular features critical for drug-target interactions.GCNs provide deep insights into molecular interactions via relational data,whereas transformers increase prediction accu-racy by capturing complex dependencies within biological sequences.Network-based models offer a systematic perspective by integrating diverse data sources,and traditional ML efficiently handles large datasets to improve overall predictive accuracy.Collectively,these AI-driven methods are transforming drug-target predictions and advancing the development of personalized therapy.This review summa-rizes the application of AI in drug development,particularly in drug-target prediction,and offers rec-ommendations on models and algorithms for researchers engaged in biomedical research.It also provides typical cases to better illustrate how AI can further accelerate development in the fields of biomedicine and drug discovery.

Traditional Chinese medicine (TCM) is characterized by complex, multicomponent herbal formulations that challenge the conventional“one drug, one target” paradigm. Network pharmacology, through the construction of multilayered drug-target-disease networks, provides a systematic framework for unraveling TCM’s multitarget and multipathway mechanisms. Recent advancements in artificial intelligence, particularly large language models (LLMs), further enhance data integration, target identification, and clinical decision-making. This review synthesizes current progress in the application of network pharmacology and LLMs in TCM, highlighting their potential to deepen mechanistic insights and optimize drug discovery. By bridging traditional medical wisdom with modern computational tools, this integrative approach aims to advance the scientific validation of TCM and foster innovative healthcare solutions.

Objective:To construct and validate a prognostic model of colon cancer based on differentially expressed anoikis-related genes, and to preliminarily investigate the relationship between anoikis-related genes and the tumor immune microenvironment of colon cancer.Methods:A total of 472 cancer tissues samples of patients with colon cancer, RNA sequencing data and clinical data of 41 normal tissues samples were downloaded from the Cancer Genome Atlas (TCGA) database between the establishment time and July in 2024. A total of 919 genes related to anoikis were screened out from GeneCards database, and the common genes were selected from the RNA sequencing gene datasets of colon cancer and normal colon tissues in the TCGA database, among which the differentially expressed anoikis-related genes of colon cancer and normal colon tissues were screened out based on P < 0.05. Furthermore, genes related to the prognosis of 446 colon cancer patients with prognostic data in the TCGA database were screened by using univariate Cox proportional risk model; the genes with P < 0.05 were further screened out and a colon cancer prognosis model was constructed by using LASSO-Cox proportional risk model. The risk score of the above 446 colon cancer patients in the TCGA database was calculated according to the prognostic model, and the patients were divided into high-risk (≥ median value) group and low-risk (< median value) group according to the median risk score, and the overall survival of the 2 groups was analyzed by using the Kaplan-Meier method. The risk score based on R software-based time ROC program package was used to predict 1-year, 2-year, 3-year overall survival therapeutic efficacy of colon cancer patients in the TCGA database. According to the median risk score of colon cancer patients in the TCGA database, the patients in the International Cancer Genome Consortium (ICGC) database were divided into high-risk group and low-risk group. Kaplan-Meier method and receiver operating characteristic (ROC) curve were used to verify the predictive effect of the prognostic model. The differentially expressed genes between low-risk group and high-risk group stratified by prognostic model risk score in the TCGA database were used to perform single sample gene set enrichment analysis (ssGESA) of immune cells and immune function by using R software related programs. The differences in risk scores of patients with different immunophenotypes (including inflammator response type, wound healing type, interferon gamma dominant type and lymphocyte depletion type) were compared; and correlation analysis of infiltration and risk scores between immune cells and stromal cells in tumor microenvironment was made. Based on the tumor immune function and rejection (TIDE) database, the relationship between the prognostic model risk score and programmed death receptor ligand 1 (PD-L1) gene expression level was analyzed. Results:Based on anoikis-related genes in the GeneCards database, 236 differentially expressed anoikis-related genes between colon cancer tissues and normal tissues were obtained from the TCGA database. LASSO Cox regression was applied to establish a prognostic model constructed by 7 differentially expressed anoikis-related genes in cancer tissues and normal colon tissues related to the prognosis of colon cancer. Risk score = 0.366×TIMP1-0.404×NAT1+0.207×LTB4R2+0.075×INHBB+0.140×CD36-0.109×MMP3+2.994×OFCC1. The median risk score of 446 colon cancer patients in the TCGA database was 1.754 719 545. Survival analysis showed that the overall survival of colon cancer patients in high-risk group of the TCGA database was worse than that in low-risk group ( P < 0.001); ROC curve analysis showed that the area under the curve for predicting 1-year, 2-year and 3-year overall survival of patients in the TCGA database based on the prognostic model risk score was 0.705, 0.731 and 0.723, respectively. Kaplan-Meier method analysis showed that in the ICGC database, the overall survival of colon cancer patients in high-risk group was worse than that in low-risk group ( P = 0.041); ROC curve analysis showed that the area under the curve of prognostic model risk score for predicting 1-year and 2-year overall survival of colon cancer patients in the ICGC database was 0.663 and 0.966, respectively. ssGESA analysis showed that macrophage level in high-risk group was higher than that in low-risk group, helper T (Th) 1 cell and Th2 cell levels in high-risk group were lower than those in low-risk group (all P < 0.01). In terms of immune function, the cell killing activity and histocompatibility complex Ⅰ level in high-risk group were lower than those in low-risk group, and type Ⅱ interferon response score in high-risk group was higher than that in low-risk group (all P < 0.05). The analysis of immunophenotype showed that the risk score of inflammatory response type was higher than that of wound healing type ( P < 0.05), and there was no statistically significant difference in risk score between the other 2 types (all P > 0.05). Risk score was positively correlated with stromal cell infiltration score ( R = 0.340, P < 0.001) and immune cell infiltration score ( R = 0.148, P < 0.05); the expression level of PD-L1 in high-risk group was higher than that in low-risk group in the TCGA database ( P = 0.048), and the expression level of PD-L1 was positively correlated with risk score ( R = 0.130, P = 0.009). Conclusions:A prognostic model of colon cancer constructed by anoikis-related genes can better predict the prognosis of colon cancer patients, and anoikis-related genes may play an important role in tumor immunity of colon cancer.