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.

Eye diagnosis is a method for inspecting systemic diseases and syndromes by observing the eyes.With the development of intelligent diagnosis in traditional Chinese medicine(TCM),artificial intelligence(AI)can improve the accuracy and efficiency of eye diagnosis.However,the research on intelligent eye diagnosis still faces many challenges,including the lack of standardized and precisely labeled data,multi-modal information analysis,and artificial in-telligence models for syndrome differentiation.The widespread application of AI models in medicine provides new insights and opportunities for the research of eye diagnosis intelli-gence.This study elaborates on the three key technologies of AI models in the intelligent ap-plication of TCM eye diagnosis,and explores the implications for the research of eye diagno-sis intelligence.First,a database concerning eye diagnosis was established based on self-su-pervised learning so as to solve the issues related to the lack of standardized and precisely la-beled data.Next,the cross-modal understanding and generation of deep neural network models to address the problem of lacking multi-modal information analysis.Last,the build-ing of data-driven models for eye diagnosis to tackle the issue of the absence of syndrome dif-ferentiation models.In summary,research on intelligent eye diagnosis has great potential to be applied the surge of AI model applications.

Objective To investigate the reliability and validity of the assessment tools of Brief ICF Core Sets for Arthroplasty of Knee Osteoarthritis in Perioperative Period(ICSAKOPP). Methods From May,2022 to April,2023,320 patients undergoing knee arthroplasty were selected in Peking University Third Hospital,China-Japan Friendship Hospital,Peking University First Hospital and Chinese PLA General Hospital.Trained assessors used Brief ICSAKOPP to evaluate all enrolled patients before arthroplasty,three days(±one day)after arthroplasty,three weeks(±one week)after arthroplasty,and three months(±one month)after ar-throplasty.Western Ontario and McMaster Universities Osteoarthritis Index(WOMAC)scores were recorded at the same time.Five professionals were asked to score all the items of Brief ICSAKOPP,and the content validity index(CVI)was caculated. Results A total of 64 cases were dropped down.CVI of all the items of the Brief ICSAKOPP were above 0.8,with a av-erage CVI of the scale of 0.938.The Cronbach's α coefficient of the Brief ICSAKOPP was 0.813.There was a moderate correlation(r=0.681,P<0.001)between the overall Brief ICSAKOPP and WOMAC scores,as well as body functional dimension score(r=0.668,P<0.001)and activities and participation dimension score(r=0.657,P<0.001). Conclusion Brief ICSAKOPP is good in content validity,internal consistency reliability and criterion validity.

Objective:To investigate the factors affecting the change of serum calcium and phosphorus after total parathyroidectomy for secondary hyperparathyroidism(SHPT).Method:Sixty patients with secondary hyperparathyroidism undergoing total parathyroidectomy were selected.Gender, age, hemodialysis time, bone pain time, intravenous calcium supplementation time, blood parathyroid hormone(PTH), parathyroid mass, blood calcium, phosphorus and alkaline phosphatase at admission were tested to compare the correlation between postoperative calcium and phosphorus changes.Result:Blood calcium was: (2.50±0.02), (2.27±0.03), (1.97±0.03), (1.95±0.03), (1.95±0.03), (2.03±0.02) mmol/L at admission, 6 hours after surgery, 1, 2, 3 days after surgery, and when discharged. Blood phosphorus was: (2.32±0.07), (1.95±0.05), (1.65±0.06), (1.44±0.05), (1.35±0.36), (1.14±0.04) mmol/L and when discharged.There are differences between pairwise comparisons ( F=100.979, P<0.05; F=118.910, P<0.05). The blood PTH was (1 791±760) pg/ml, and the postoperative intravenous calcium supplementation time was (7.13±2.34) days. Blood PTH was positively correlated with bone pain time, blood phosphorus and parathyroid mass at admission ( t=-2.199, P<0.05; t=4.718, P<0.05; t=2.507, P<0.05). The time of intravenous calcium supplementation was positively correlated with ALP at admission ( t=3.098, P<0.05). Preoperative serum PTH > 1 430 pg/ml or ALP > 191 mmol/L were prone to hypocalcemia on the first day after surgery. Conclusions:The longer bone pain time, the higher blood phosphorus and the more obvious parathyroid hyperplasia were, the higher PTH was. The higher the ALP, the longer the time of intravenous calcium supplementation. Preoperative serum PTH and ALP values can be used as predictors of hypocalcemia on the first day after surgery.

Objective:To explore the medication law of TCM compounds used in the treatment of non-small cell lung cancer (NSCLC).Methods:Clinical literature about TCM compounds in the treatment of NSCLC was retrieved from CNKI, Wanfang Data, PubMed and Web of Science core collection database from January 1st, 2003 to May 1st, 2023. The Python and the ancient and modern medical record cloud platform V2.3.7 were used to analyses frequency statistics and properties, and clustering analysis, factor analysis and association rule analysis were performed.Results:A total of 866 articles were included, with 660 effective TCM compounds, 647 kinds of Chinese materia medica. There were 36 kinds of high-frequency Chinese materia medica, such as Astragali Radix, Atractylodis Macrocephalae Rhizoma and Poria; the efficacy categories of Chinese materia medica mainly included tonics and phlegm resolving cough relieving and asthma relieving drugs. Cluster analysis obtained 4 clusters, factor analysis extracted 11 common factors, and association rule analysis obtained 37 highly supported combinations of Chinese materia medica. The most reliable triple drug combination among them was Astragali Radix-Atractylodis Macrocephalae Rhizoma-Hedyotis diffusa willd, and the double drug combination was Astragali Radix-Ligustri Lucidi Fructus.Conclusion:TCM for the treatment of NSCLC mainly focuses on strengthening the healthy qi, clearing heat, resolving phlegm and detoxifying toxins, supplemented by tonifying qi and activating blood circulation, dispelling dampness and resolving phlegm.

The comparison between traditional Chinese medicine Jinzhen oral liquid (JZOL) and Western medicine in treating children with acute bronchitis (AB) showed encouraging outcomes. This trial evaluated the efficacy and safety of the JZOL for improving cough and expectoration in children with AB. 480 children were randomly assigned to take JZOL or ambroxol hydrochloride and clenbuterol hydrochloride oral solution for 7 days. The primary outcome was time-to-cough resolution. The median time-to-cough resolution in both groups was 5.0 days and the antitussive onset median time was only 1 day. This randomized controlled trial showed that JZOL was not inferior to cough suppressant and phlegm resolving western medicine in treating cough and sputum and could comprehensively treat respiratory and systemic discomfort symptoms. Combined with clinical trials, the mechanism of JZOL against AB was uncovered by network target analysis, it was found that the pathways in TRP channels like IL-1β/IL1R/TRPV1/TRPA1, NGF/TrkA/TRPV1/TRPA1, and PGE2/EP/PKA/TRPV1/TRPA1 might play important roles. Animal experiments further confirmed that inflammation and the immune regulatory effect of JZOL in the treatment of AB were of vital importance and TRP channels were the key mechanism of action.

Objective:To investigate the factors related to recurrent laryngeal nerve invasion in papillary thyroid carcinoma (PTC) with posterior capsular involvment.Methods:The data of 186 PTC patients admitted and operated from Jun 2017 to Jun 2022 were retrospectively analyzed. The invasion of recurrent laryngeal nerve was evaluated on its relation to gender, age, tumor size, Hashimoto's thyroiditis, lymph node metastasis in central region, BRAFV600E gene mutation especially PTC posterior capsular involvement.Results:The recurrent laryngeal nerve was invaded in 30 out of 186 patients. Univariate analysis showed that recurrent laryngeal nerve invasion was related to tumor size, Hashimoto's thyroiditis and cervical lymph node metastasis( χ2=6.964,4.814,6.078, P<0.05). Multivariate regression analysis showed that tumor size and lymph node metastasis in cervical region were independent risk factors for recurrent laryngeal nerve invasion(β=1.020,1.622, P<0.05). Hashimoto's thyroiditis was a protective factor for recurrent laryngeal nerve invasion (β=-1.881, P<0.05). Conclusions:When papillary thyroid carcinoma invaded the capsule, the risk of recurrent laryngeal nerve invasion was higher with larger tumor size and cervical lymph node metastasis, while Hashimoto's thyroiditis was a protective factor for the risk of recurrent nerve invasion.

METTL3 and METTL14 are two components that form the core heterodimer of the main RNA m6A methyltransferase complex (MTC) that installs m6A. Surprisingly, depletion of METTL3 or METTL14 displayed distinct effects on stemness maintenance of mouse embryonic stem cell (mESC). While comparable global hypo-methylation in RNA m6A was observed in Mettl3 or Mettl14 knockout mESCs, respectively. Mettl14 knockout led to a globally decreased nascent RNA synthesis, whereas Mettl3 depletion resulted in transcription upregulation, suggesting that METTL14 might possess an m6A-independent role in gene regulation. We found that METTL14 colocalizes with the repressive H3K27me3 modification. Mechanistically, METTL14, but not METTL3, binds H3K27me3 and recruits KDM6B to induce H3K27me3 demethylation independent of METTL3. Depletion of METTL14 thus led to a global increase in H3K27me3 level along with a global gene suppression. The effects of METTL14 on regulation of H3K27me3 is essential for the transition from self-renewal to differentiation of mESCs. This work reveals a regulatory mechanism on heterochromatin by METTL14 in a manner distinct from METTL3 and independently of m6A, and critically impacts transcriptional regulation, stemness maintenance, and differentiation of mESCs.
Animals ; Mice ; Methylation ; Chromatin ; Histones/metabolism* ; RNA, Messenger/genetics* ; Methyltransferases/metabolism* ; RNA/metabolism*