Digital and intelligent technologies serve as the core engine driving the inheritance of the essence and the innovation while upholding the fundamentals of traditional Chinese medicine（TCM）. Currently， the digital and intelligent transformation of TCM has undergone four developmental stages， exhibiting inherent characteristics such as long-term inevitability， objective standardization， and ecological evolution. By introducing quantitative metrics， digital and intelligent technologies have achieved breakthroughs in TCM knowledge inheritance and innovation， clinical diagnosis and treatment， and herbal medicine supply. The practical applicability of methodological innovations has been empirically validated， though significant disparities exist in technological adaptability and application depth across different fields. Overall， the digital and intelligent transformation of TCM remains in its nascent stage， grappling with multiple structural challenges：weak data foundations， inadequate technological adaptability， incomplete institutional frameworks， shortages of multidisciplinary talent， lagging policies and regulations， and urban-rural digital divide. In order to foster sustainable development and modernization of TCM， this paper establishes a six-dimensional collaborative governance framework of encompassing data， technology， organization， institutions， environment and ethics， which is rooted in data governance and digital governance theories. Future efforts should center on standardization， integration， and ecosystem development to build a data and technology foundation. Focus should be placed on deepening innovation and application of key TCM-specific technologies， while simultaneously strengthening interdisciplinary talent cultivation， improving institutional mechanisms and policy frameworks， and increasing support for rural areas. By adopting a people-centered and technology-empowered approach， we can overcome developmental constraints and unleash the powerful driving force of digital and intelligent technologies for the inheritance of TCM.

Purpose: The aim of this study is to determine the impact of solute carrier family 3 member 2 (SLC3A2) on the malignant phenotype of gliomas and its role in regulating ferroptosis sensitivity. Materials and Methods: The malignant phenotype of glioma was assessed by cell proliferation assay, colony formation assay, EdU assay, wound healing, and Transwell experiments. We further validated the impact of reduced SLC3A2 expression on the sensitivity to ferroptosis in glioma cells through Cell Counting Kit-8 assays, flow cytometry, western blotting, and transmission electron microscopy. Western blot was used to explore how SLC3A2 affects glioma sensitivity to ferroptosis through the AKT/NF-E2-related factor 2 (NRF2)/glutathione peroxidase 4 (GPX4) axis. By establishing a subcutaneous xenograft tumor model in BALB/c-nude mice, we investigated the growth of tumors following the knockout of SLC3A2 in glioma cells. Results: Downregulation of SLC3A2 suppressed the malignant phenotype of glioma by blocking the cell cycle and epithelial-mesenchymal transition processes. On the other hand, loss of SLC3A2 not only downregulated SLC7A11 but also prevented the activation of the AKT/NRF2/GPX4 axis. These lead to increased accumulation of reactive oxygen species and lipid peroxides, ultimately enhancing the susceptibility of glioma to ferroptosis. Conclusion Our findings suggest that SLC3A2 is an oncogene in gliomas, promoting their occurrence and development. It plays a critical role in ferroptosis resistance through the AKT/NRF2/GPX4 axis.

ObjectiveTo enhance the genetic diagnostic yield for fetal short femur length （FL） and to evaluate the integration of whole exome sequencing （WES） with chromosomal abnormality detection in prenatal diagnosis. MethodsA total of 27 mid-to-late trimester fetuses diagnosed with short FL ［FL < 5th percentile for gestational age or < -2 SD （Standard Deviation）］ at the Prenatal Diagnosis And Medical Genetics Center，Shenshan Medical Center，Memorial Hospital of Sun Yat-Sen University between November 2023 and May 2025 were included. Amniotic fluid or umbilical cord blood samples were collected and subjected to chromosomal karyotyping （19cases）， chromosomal microarray analysis （CMA） or low-coverage whole genome copy number variation（CNV-Seq） （27 cases）， and trio-based whole exome sequencing （trio-WES） （27 cases）. The pathogenicity of genetic variants were interpreted according to the American College of Medical Genetics and Genomics （ACMG） guidelines. ResultsTwenty-six genomic variants were detected in 17 of the 26 fetuses， including numerical chromosomal abnormalities （mosaic type）， copy number variations （CNVs）， single nucleotide variants （SNVs） and insertions-deletions （INDELs）. Genomic variants detected in 11 cases could explain the short femur phenotype.The overall diagnostic rate was 11/27， with pathogenic/likely pathogenic variants explaining 9 cases and variants of uncertain significance （VUS） accounting for 1 cases. In addition Chromosomal karyotype analysis revealed chromosome mosaicism explaining 1 case. The diagnostic rate of CNV detection （CMA/CNV-seq） was 2/27， while WES achieved a diagnostic rate of 10/27， with an incremental yield of 8/27 in cases with negative or inconclusive CNV results. Pathogenic mutations involved genes such as FGFR3， SHOX， DUOX2， CLCN5， and HBA1/HBA2. ConclusionTrio-WES significantly improves the genetic diagnostic rate for fetal short femur length， particularly in cases where chromosomal analysis fails to identify a cause. Karyotyping remains valuable for detecting low-level mosaicism in large-scale chromosomal abnormalities， thereby complementing WES and CNV detection. A stepwise or parallel genetic testing strategy is recommended in prenatal diagnosis to optimize diagnostic efficiency and support clinical decision-making.

Obstructive sleep apnea (OSA) is an increasingly widespread sleep-breathing disordered disease, and is an independent risk factor for many high-risk chronic diseases such as hypertension, coronary heart disease, stroke, arrhythmias and diabetes, which is potentially fatal. The key to the prevention and treatment of OSA is early diagnosis and treatment, so the assessment and diagnostic technologies of OSA have become a research hotspot. This paper reviews the research progresses of severity assessment parameters and diagnostic technologies of OSA, and discusses their future development trends. In terms of severity assessment parameters of OSA, apnea hypopnea index (AHI), as the gold standard, together with the percentage of duration of apnea hypopnea (AH%), lowest oxygen saturation (LSpO₂), heart rate variability (HRV), oxygen desaturation index (ODI) and the emerging biomarkers, constitute a multi-dimensional evaluation system. Specifically, the AHI, which measures the frequency of sleep respiratory events per hour, does not fully reflect the patients’ overall sleep quality or the extent of their daytime functional impairments. To address this limitation, the AH%, which measures the proportion of the entire sleep cycle affected by apneas and hypopneas, deepens our understanding of the impact on sleep quality. The LSpO₂ plays a critical role in highlighting the potential severe hypoxic episodes during sleep, while the HRV offers a different perspective by analyzing the fluctuations in heart rate thereby revealing the activity of the autonomic nervous system. The ODI provides a direct and objective measure of patients’ nocturnal oxygenation stability by calculating the number of desaturation events per hour, and the biomarkers offers novel insights into the diagnosis and management of OSA, and fosters the development of more precise and tailored OSA therapeutic strategies. In terms of diagnostic techniques of OSA, the standardized questionnaire and Epworth sleepiness scale (ESS) is a simple and effective method for preliminary screening of OSA, and the polysomnography (PSG) which is based on recording multiple physiological signals stands for gold standard, but it has limitations of complex operations, high costs and inconvenience. As a convenient alternative, the home sleep apnea testing (HSAT) allows patients to monitor their sleep with simplified equipment in the comfort of their own homes, and the cardiopulmonary coupling (CPC) offers a minimal version that simply analyzes the electrocardiogram (ECG) signals. As an emerging diagnostic technology of OSA, machine learning (ML) and artificial intelligence (AI) adeptly pinpoint respiratory incidents and expose delicate physiological changes, thus casting new light on the diagnostic approach to OSA. In addition, imaging examination utilizes detailed visual representations of the airway’s structure and assists in recognizing structural abnormalities that may result in obstructed airways, while sound monitoring technology records and analyzes snoring and breathing sounds to detect the condition subtly, and thus further expands our medical diagnostic toolkit. As for the future development directions, it can be predicted that interdisciplinary integrated researches, the construction of personalized diagnosis and treatment models, and the popularization of high-tech in clinical applications will become the development trends in the field of OSA evaluation and diagnosis.

The establishment of modern biliary surgery system, alongside pivotal scientific paradigm shifts, has heralded a new era featured by precision, personalization, life-cycle care, and multidisciplinary management in the treatment of both benign and malignant biliary diseases. However, two formidable challenges persist in haunting the treatment of biliary diseases: (1) The refinement of surgical techniques has reached a plateau in reducing the disability associated with benign biliary conditions and in improving survival outcomes in biliary tract cancers; (2) Traditional evidence-based clinical studies have shown limited power in addressing complex dilemmas, such as determining whether to excise or preserve pathological gallbladders or selecting the optimal biliary drainage strategy. Consequently, the authors propose the conceptual framework of “biliary ecosystem”. In this model, diverse and abundant cholangiocytes represent forest, while blood vessels, nerves, and lymphatic vessels serve as nurturing soil, biliary stem cells function as seeds, bile flows like river network, and hepatocytes mark the river′s origins. Both benign and malignant biliary diseases exhibit significant spatiotemporal dynamics. The bile ducts form the “macro” environment, bile constitutes the “sub-macro” environment, and diverse cellular niches create the microenvironment. Specific pathological biliary conditions are shaped by intricate regulatory mechanisms that operate across these three tiers. Within the biliary ecosystem, cellular subpopulations exist remarkable diversity with states of homeostasis, oscillation, perturbation, or imbalance, underpinned by complex signaling networks. This holistic approach allows us to reframe and critically examine the pressing scientific issues confronting biliary tract diseases. Based on this framework, the authors distill key scientific questions and offer preliminary recommendations for embracing the paradigm shift. The authors anticipate that this conceptual model will promote interdisciplinary integration and accelerate clinical and translational researches.

The establishment of modern biliary surgery system, alongside pivotal scientific paradigm shifts, has heralded a new era featured by precision, personalization, life-cycle care, and multidisciplinary management in the treatment of both benign and malignant biliary diseases. However, two formidable challenges persist in haunting the treatment of biliary diseases: (1) The refinement of surgical techniques has reached a plateau in reducing the disability associated with benign biliary conditions and in improving survival outcomes in biliary tract cancers; (2) Traditional evidence-based clinical studies have shown limited power in addressing complex dilemmas, such as determining whether to excise or preserve pathological gallbladders or selecting the optimal biliary drainage strategy. Consequently, the authors propose the conceptual framework of “biliary ecosystem”. In this model, diverse and abundant cholangiocytes represent forest, while blood vessels, nerves, and lymphatic vessels serve as nurturing soil, biliary stem cells function as seeds, bile flows like river network, and hepatocytes mark the river′s origins. Both benign and malignant biliary diseases exhibit significant spatiotemporal dynamics. The bile ducts form the “macro” environment, bile constitutes the “sub-macro” environment, and diverse cellular niches create the microenvironment. Specific pathological biliary conditions are shaped by intricate regulatory mechanisms that operate across these three tiers. Within the biliary ecosystem, cellular subpopulations exist remarkable diversity with states of homeostasis, oscillation, perturbation, or imbalance, underpinned by complex signaling networks. This holistic approach allows us to reframe and critically examine the pressing scientific issues confronting biliary tract diseases. Based on this framework, the authors distill key scientific questions and offer preliminary recommendations for embracing the paradigm shift. The authors anticipate that this conceptual model will promote interdisciplinary integration and accelerate clinical and translational researches.

Objective To explore the recognition capabilities of electronic nose combined with machine learning in identifying the breath odor map of benign and malignant pulmonary nodules and Traditional Chinese Medicine (TCM) syndrome elements. Methods The study design was a single-center observational study. General data and four diagnostic information were collected from 108 patients with pulmonary nodules admitted to the Department of Cardiothoracic Surgery of Hospital of Chengdu University of TCM from April 2023 to March 2024. The patients' TCM disease location and nature distribution characteristics were analyzed using the syndrome differentiation method. The Cyranose 320 electronic nose was used to collect the odor profiles of oral exhalation, and five machine learning algorithms including random forest (RF), K-nearest neighbor (KNN), logistic regression (LR), support vector machine (SVM), and eXtreme gradient boosting (XGBoost) were employed to identify the exhaled breath profiles of benign and malignant pulmonary nodules and different TCM syndromes. Results (1) The common disease locations in pulmonary nodules were ranked in descending order as liver, lung, and kidney; the common disease natures were ranked in descending order as Yin deficiency, phlegm, dampness, Qi stagnation, and blood deficiency. (2) The electronic nose combined with the RF algorithm had the best efficacy in identifying the exhaled breath profiles of benign and malignant pulmonary nodules, with an AUC of 0.91, accuracy of 86.36%, specificity of 75.00%, and sensitivity of 92.85%. (3) The electronic nose combined with RF, LR, or XGBoost algorithms could effectively identify the different TCM disease locations and natures of pulmonary nodules, with classification accuracy, specificity, and sensitivity generally exceeding 80.00%.Conclusion Electronic nose combined with machine learning not only has the potential capabilities to differentiate the benign and malignant pulmonary nodules, but also provides new technologies and methods for the objective diagnosis of TCM syndromes in pulmonary nodules.

Background: Inhalation-based combination therapy has gained increasing attention for local treatments. However, a key challenge remains in ensuring the sustained pulmonary release of multiple active ingredients, particularly in traditional Chinese medicine (TCM) formulations. Objective: This study investigates a novel PulmoSphere-based inhalable carrier designed for the sustained pulmonary release of multiple active ingredients, using Shuang-Huang-Lian as a model TCM formulation containing three chemical markers. Materials and methods: The carrier was developed using PulmoSphere technology, incorporating phospholipid complexes of the chemical markers and hyaluronic acid (HA) into spray-dried microparticles. The aerodynamic properties, release characteristics, pulmonary distribution, and anti-inflammatory efficacy of different formulations were evaluated in vitro and in mice. Results: The microparticles retained the excellent aerodynamic properties of conventional PulmoSphere particles, with a mass median aerodynamic diameter of approximately 3.1 μm and a fine particle fraction of approximately 55%. Compared to free Shuang-Huang-Lian or phospholipid complex-loaded PulmoSphere particles, the HA-containing particles prolonged the retention of chemical markers in the lung epithelial lining fluid, demonstrating sustained release in vivo. Additionally, the HA-containing formulation enhanced the exposure of the three chemical markers to immune cells and lung tissues, leading to improved and prolonged anti-inflammatory effects, even at decreased doses. Conclusion: This novel inhalable particle system represents a promising approach for sustained pulmonary co-delivery of multiple active ingredients, offering enhanced and extended local therapeutic efficacy.

Objective To analyze the research status and hotspots in the field of astragalus polysaccharides;To provide references for further research.Methods Research literature about astragalus polysaccharides was retrieved from CNKI,Wanfang Data,VIP,PubMed,and Web of Science databases from 1st,Jan.2013 to 1st,July 2023.NoteExpress 3.7 software was used to manage the literature and ultimately establish a database.Excel 2019,CiteSpace 6.2.2R and VOSviewer 1.6.18 were used to visually analyze the publication volume,authors,institutions,and keywords of the included literature.Results A total of 2 462 articles were included,with 1 284 Chinese articles and 1 178 English articles.The main research institutions were Gansu University of Chinese Medicine,Shandong University of Traditional Chinese Medicine,and Beijing University of Chinese Medicine.The core authors of Chinese literature were Liu Yongqi,Wang Hongxin,Lu Meili,etc.The core authors of English literature included Zhang Wei,Li Ke,Yang Xiaojun,etc.High-frequency keywords of Chinese literature included Astragali Radix,rats,polysaccharides,cell apoptosis,and oxidative stress,etc.High frequency keywords in English literature included expression,in vitro,oxidative stress,apoptosis,etc.Conclusion The research on astragalus polysaccharides focuses on their pharmacological effects and mechanisms.Intestinal flora,immune regulation,autophagy and apoptosis are the hot action mechanisms in this field.The focus of disease research involves tumor and diabetes,and antiviral,anti infection and other pharmacological effects are the research trend.

Cardiovascular disease (CVD), chronic kidney disease (CKD), and metabolic disorders are the 3 major chronic diseases threatening human health, which are closely related and often coexist, significantly increasing the difficulty of disease management. In response, the American Heart Association (AHA) proposed a novel disease concept of “cardiovascular-kidney-metabolic (CKM) syndrome” in October 2023, which has triggered widespread concern about the co-treatment of heart and kidney diseases and the prevention and treatment of metabolic disorders around the world. This review posits that effectively managing CKM syndrome requires a new and multidimensional paradigm for diagnosis and risk prediction that integrates biological insights, advanced technology and social determinants of health (SDoH). We argue that the core pathological driver is a “metabolic toxic environment”, fueled by adipose tissue dysfunction and characterized by a vicious cycle of systemic inflammation and oxidative stress, which forms a common pathway to multi-organ injury. The at-risk population is defined not only by biological characteristics but also significantly impacted by adverse SDoH, which can elevate the risk of advanced CKM by a factor of 1.18 to 3.50, underscoring the critical need for equity in screening and care strategies. This review systematically charts the progression of diagnostic technologies. In diagnostics, we highlight a crucial shift from single-marker assessments to comprehensive multi-marker panels. The synergistic application of traditional biomarkers like NT-proBNP (reflecting cardiac stress) and UACR (indicating kidney damage) with emerging indicators such as systemic immune-inflammation index (SII) and Klotho protein facilitates a holistic evaluation of multi-organ health. Furthermore, this paper explores the pivotal role of non-invasive monitoring technologies in detecting subclinical disease. Techniques like multi-wavelength photoplethysmography (PPG) and impedance cardiography (ICG) provide a real-time window into microcirculatory and hemodynamic status, enabling the identification of early, often asymptomatic, functional abnormalities that precede overt organ failure. In imaging, progress is marked by a move towards precise, quantitative evaluation, exemplified by artificial intelligence-powered quantitative computed tomography (AI-QCT). By integrating AI-QCT with clinical risk factors, the predictive accuracy for cardiovascular events within 6 months significantly improves, with the area under the curve (AUC) increasing from 0.637 to 0.688, demonstrating its potential for reclassifying risk in CKM stage 3. In the domain of risk prediction, we trace the evolution from traditional statistical tools to next-generation models. The new PREVENT equation represents a major advancement by incorporating key kidney function markers (eGFR, UACR), which can enhance the detection rate of CKD in primary care by 20%-30%. However, we contend that the future lies in dynamic, machine learning-based models. Algorithms such as XGBoost have achieved an AUC of 0.82 for predicting 365-day cardiovascular events, while deep learning models like KFDeep have demonstrated exceptional performance in predicting kidney failure risk with an AUC of 0.946. Unlike static calculators, these AI-driven tools can process complex, multimodal data and continuously update risk profiles, paving the way for truly personalized and proactive medicine. In conclusion, this review advocates for a paradigm shift toward a holistic and technologically advanced framework for CKM management. Future efforts must focus on the deep integration of multimodal data, the development of novel AI-driven biomarkers, the implementation of refined SDoH-informed interventions, and the promotion of interdisciplinary collaboration to construct an efficient, equitable, and effective system for CKM screening and intervention.