共找到 20 条结果
Despite the growing prevalence of network models in biological and medical research, the philosophical foundations of these constructs remain elusive and insufficiently examined. Building on data-driven insights in systems biology and teleological models of integrative physiology, we have criticised agentless theory, relational ontologies, and cybernetic perspectives in biological contexts. We have posited the necessity for a philosophical advocacy of a holistic approach to biology alongside a relational epistemology. A foundational issue in network models in biology and physiology is recognising the network as the predominant meme of contemporary society, which has permeated all aspects of human life and has been cultivated within biology and physiology through modern theoretical constructs and practical applications. By discussing minimal cognition, tinkering, and stigmergy, we argued for a philosophical advocacy of the person as a relational cybernetic organism. Philosophical arguments concerning graph and relational models in biology can be resolved by embracing epistemic humility.
Basic medical sciences form the foundation for clinical competence; however, students often face difficulties in retaining theoretical knowledge due to abstract content, large class sizes, limited instructional time, and passive teaching methods. In recent years, innovative and student-centered approaches-such as flipped classrooms, small-group discussions, and teacher role-playing-have been widely adopted to promote engagement and deeper learning. Measuring student satisfaction plays a key role in evaluating the effectiveness of these methods. Existing instruments are often lengthy, context-specific, or lack robust psychometric validation. Therefore, this study aimed to develop and validate a short, reliable questionnaire to assess student satisfaction with innovative teaching methods in basic medical sciences (SSIM_sQ). This methodological study was conducted at Babol University of Medical Sciences (2022-2024) and involved 953 medical science students exposed to active learning methods. The study comprised two phases: item generation and psychometric evaluation. Ten initial items were developed based on the literature and expert review. Psychometric testing included face and content validity, exploratory and confirmatory factor analyses, and reliability assessment. Data analysis was performed using SPSS 27 and AMOS 24. The final 6-item SSIM_sQ demonstrated excellent validity and reliability. EFA supported a single-factor structure explaining 76% of variance, and CFA confirmed good model fit (RMSEA = 0.040, CFI = 1.00, TLI = 0.997). The instrument showed high convergent validity (AVE = 0.825, CR = 0.966), strong internal consistency and stability (α = 0.960, Ω = 0/960, Intraclass correlation coefficient = 0.976). The SSIM_sQ is a brief, valid, and reliable instrument for assessing student satisfaction with innovative teaching methods in basic medical sciences. Its strong psychometric properties and practicality support its use in both research and educational quality improvement.
The microbiome actively influences antimicrobial resistance (AMR) dynamics by shaping both ecological and evolutionary processes. However, the extent of its role in resistance emergence, transmission and persistence remains unclear. Traditional AMR research has mainly focused on genetic mechanisms and pathogen-level dynamics. In contrast, the intersection of AMR and the microbiome, including resistance-gene reservoirs, microbial competition and community-mediated selection, remains poorly represented, especially in a modelling context. Here we present a structured framework for incorporating microbiome-AMR interactions into predictive models. We identify key microbiome-mediated processes shaping AMR across different levels of complexity, describe how these can be quantitatively integrated into models, and identify critical data gaps that limit current approaches. By bridging microbiome ecology, AMR biology and mathematical modelling, we set out research priorities and strategies to improve resistance prediction and guide microbiome-targeted interventions.
Chikungunya virus (CHIKV), an emerging arbovirus, is transmitted by Aedes mosquitoes. Climate change and increasing population mobility have driven recent outbreaks beyond traditional endemic regions. Since early July 2025, Guangdong province in southern China has faced an unprecedented outbreak of chikungunya fever. We aim to in-depth describe the epidemic features and theoretically assess the potential impact of vaccination campaigns. In total, the outbreak reported more than 25,000 cases. Foshan and Jiangmen successively emerged as epicenters of the outbreak. Through stringent public health interventions, the outbreak was controlled within two months in these two epicenters, respectively. Phylogenetic analysis revealed close genetic relation of the CHIKV isolates from this outbreak to the recent isolates in Réunion and Mayotte, but distant from the ones previously identified in China. Pre-emptive vaccination, achieving 20, 40%, 60%, and 80% coverage pre-outbreak, would avert up to 57%, 81%, 92%, and 97% infections shown by mathematical modelling, respectively. Achieving a daily vaccination rate comparable to the COVID-19 rollout, covering approximately 4% of the population in Foshan and 2% of the population in Jiangmen per day, could lower cumulative CHIKV infections by 68.7% in Foshan within two months and by 98.4% in Jiangmen within four months, respectively. In summary, the 2025 chikungunya outbreak in Guangdong was likely sparked by case importation. Implementation of stringent public health interventions is possible to control the outbreak, but can provoke significant public concerns. Vaccine campaigns are expected to be effective in both preparedness and response to future chikungunya outbreaks.
Melanoma is an aggressive malignancy with rising global incidence. While early surgical intervention improves survival in localized cases, treatment resistance and recurrence remain a challenge. This underscores the critical need to identify prognostic biomarkers for early diagnosis, personalized treatment, and novel therapeutic development. The GSE126076 and melanoma dataset (Skin Cutaneous Melanoma, TCGA, PanCancer Atlas) were analyzed using an information-theoretical method to identify prognostic factors. Survival analysis was performed via Kaplan-Meier curves and log-rank tests to compare high- and low-mRNA expression groups. In vitro, A375 cells and A2058 cells were treated with the GGT inhibitor 6-diazo-5-oxo-L-norleucin (DON). Cell viability was assessed using the CCK-8 assay and intracellular GSH levels were measured following treatment. Through information-theoretic analysis and survival analysis, we identified GGT6 as a prognostic gene in melanoma. Survival analysis revealed that high GGT6 expression was significantly associated with shorter disease-specific survival across all disease stages. In vitro, 10 μM DON for 72 h reduced A375 cell proliferation by 90.72% versus control (p<0.0001), with an IC50 of 5.14 μM; and reduced A2058 cell proliferation by 60.93% (p<0.0001), with an IC50 of 7.93 μM. Measurement of GSH levels of A375 and A2058 cells revealed that melanoma cells treated with 10 μM DON exhibited lower GSH levels compared with the respective control groups (p<0.0001 and p=0.0042, respectively). Our study is the first to demonstrate the association between GGT6 expression levels and melanoma prognosis, and reveals that GGT inhibition suppresses melanoma cell viability. These findings provide new insights into the mechanisms of melanoma development and progression, and suggest GGT as a potential therapeutic target for clinical treatment.
Residents in pediatric endocrinology subspecialty units encounter diverse educational scenarios spanning theory, skills, and attitudes; yet, brief residencies frequently limit their exposure to certain clinical cases. Research in medical education demonstrates that e-learning can address such challenges efficiently. We implemented a blended learning model grounded in the Kolb learning cycle that uses structured, case-based e-learning. We aimed to evaluate the utility and usability of blended learning using a novel e-learning tool. We used a problem-solving approach and used the physical separation of case-based e-learning (interactive, patient scenario-based online modules) and theoretical content delivery as the educational model for residents in a pediatric endocrinology and diabetology unit. Residents worked asynchronously (on their own time, not simultaneously with others) on clinical scenarios and completed formative assessments (practice tests designed to provide feedback for learning rather than grades) with immediate feedback using a flipped classroom teaching method, in which students review material before group instruction. In addition, all cases could be discussed with specialists during face-to-face learning opportunities through a blended learning approach that combines online and in-person elements. We evaluated Kirkpatrick level 1 (reaction, how participants respond to training) and level 2 (learning, measured as an increase in knowledge or capability) outcomes using the postgraduate Medical E-learning Evaluation Survey (MEES) and the User Experience Questionnaire (UEQ), which assesses users' perceptions of e-learning platforms. Questionnaires from 12 pediatric residents and 1 questionnaire from a fourth-year medical student were evaluated. The main strengths identified were the tool's support for applying content to daily clinical work (12/13, 92% users), provision of timely summaries (n=9, 69% users), access to reliable information sources (n=9, 69% users), and immediate feedback on responses (n=8, 62% users). Key weaknesses included device compatibility for e-learning (n=5, 38% users), limited content personalization (n=4, 31% users), and a lack of a navigation aid (n=4, 31% users). No significant functional issues were reported. The UEQ evaluation showed that dependability received the lowest rating, while attractiveness and stimulation received the highest rating. Our e-learning proposal provides a practical way to apply theoretical knowledge through interactive clinical cases. Evaluations show that users are highly motivated to engage with e-learning, highlighting our tool's adaptability and effectiveness for postgraduate medical education in pediatric endocrinology. Identifying strengths and weaknesses will guide future improvements. Evaluating various aspects of e-learning remains crucial, as these aspects can affect learning outcomes. However, more longitudinal evaluations of e-learning are necessary to achieve a comprehensive understanding of its effectiveness.
Cervical cancer, driven mainly by human papillomavirus (HPV) infection, remains one of the most common malignant tumors among women worldwide, posing significant challenges in treatment and drug development. Traditional two-dimensional (2D) cell culture models fail to accurately replicate the in vivo tumor microenvironment (TME), especially HPV-driven oncogenic signaling, immune contexture, and stromal interactions unique to cervical cancer, limiting their predictive value for therapeutic efficacy (Y. Liu, H. Ai. Comprehensive insights into human papillomavirus and cervical cancer: pathophysiology, screening, and vaccination strategies. Biochim Biophys Acta Rev Cancer. 2024;1879(6):189192). Hydrogels have emerged as promising biomaterials for constructing three-dimensional (3D) tumor models due to their tunable physicochemical properties, excellent biocompatibility, and ability to mimic the extracellular matrix. This review focuses on hydrogel applications in 3D cervical cancer TME modeling, with an emphasis on recapitulating HPV-driven biology, immune-stromal crosstalk, and stromal interactions, emphasizing their role in simulating key aspects of tumor biology such as cell-cell and cell-matrix interactions, hypoxia, and drug resistance. Recent advances in hydrogel-based 3D models for high-throughput drug screening are critically analyzed, highlighting their potential to improve the precision of cervical cancer treatment and accelerate novel drug discovery. However, critical challenges including high cost, limited industrial scalability, technical complexity, and strict regulatory constraints remain to be addressed to realize their full translational potential. By integrating current research findings, this review aims to provide a theoretical framework and technical guidance for future studies focused on enhancing the physiological relevance of in vitro cervical cancer models and optimizing therapeutic strategies.
The cognitive paradigm in medical education is undergoing a transition from traditional knowledge transmission to learner-centered knowledge construction. In China, this shift is aligned with the Outline of the Plan for the Construction of China into an Education Powerhouse (2024-2035), which mandates high-quality, intrinsic development in nursing curricula. While constructivist learning theory (CLT)-based teaching methods (eg, problem-based learning, case-based learning, and situational simulation) have been widely explored across Chinese nursing institutions, the evidentiary base remains geographically fragmented and methodologically heterogeneous. A systematic synthesis is required to inform national, evidence-based educational reforms. This protocol describes a systematic review and meta-analysis designed to evaluate the effectiveness of CLT-based teaching methods vs traditional lecture-based models on Chinese nursing students' theoretical knowledge, practical skills, self-directed learning ability, and critical thinking disposition. A comprehensive systematic search will be conducted across 9 electronic databases: PubMed, Web of Science, the Cochrane Library, Embase, CINAHL, China National Knowledge Infrastructure, Wanfang Data, VIP Database (Chinese Scientific and Technological Journal Database), and China Biology Medicine. The search period spans from database inception to September 27, 2025, with a planned update through June 11, 2026, before final synthesis. Randomized controlled trials and quasi-experimental studies involving Chinese nursing students will be included. Two independent reviewers will screen records, perform full-text assessment, extract data using standardized forms, and code composite CLT interventions, digital or technology-enhanced components, and cluster- or class-based designs using prespecified decision rules. Risk of bias will be assessed using the Cochrane Risk of Bias tool 2 (RoB 2) for randomized trials and the Joanna Briggs Institute critical appraisal tools for quasi-experimental studies. Meta-analysis will be performed using RevMan 5.4 and Stata 18.0, with random-effects models and prespecified subgroup and sensitivity analyses. This protocol was finalized in February 2026. A preliminary systematic search conducted on September 27, 2025, identified 990 records before deduplication. As of February 6, 2026, deduplication had been completed and title and abstract screening had been initiated. Data extraction, risk-of-bias assessment, and statistical synthesis had not yet started at the protocol stage and will be conducted only after completion of the updated search, final study selection, and full-text eligibility assessment. The final search update was scheduled through June 11, 2026, before data synthesis. The results manuscript will be submitted after completion of all prespecified review steps, with the timeline depending on the number and complexity of newly identified studies. This review will provide a robust evidentiary foundation for the strategic deployment of constructivist methodologies in Chinese nursing education, specifically addressing the needs of vocational and undergraduate programs in the era of digital transformation.
Conventional two-dimensional cell cultures predominantly grow as monolayers, lacking the complex spatial architecture of in vivo tumors. However, three-dimensional (3D) tumor culture systems can overcome these limitations by reconstituting cell-cell and cell-matrix interactions, thereby recapitulating the essential hallmarks of solid tumors, including spatial gradients of oxygen, growth factors and metabolites. 3D tumor models are essential preclinical tools in lung cancer research, providing valuable resources for studying cancer biology and developing novel anticancer drugs. The present review examines different 3D culture methods, highlighting the benefits and applications of multicellular tumor spheroids and organoid models in the screening process for anti-lung cancer drugs. The present review aims to provide a novel perspective on tumor biology and in vitro drug screening, and a theoretical basis for developing and applying 3D culture models.
Wound healing is a fundamental biological process essential to maintaining structural integrity and survival across both plant and animal life. Despite the profound evolutionary distance separating these kingdoms, wound healing provides one of those momentous occasions when these biological universes collide, revealing significant evolutionary parallels in the core mechanisms of healing, despite clear molecular and physiological differences. However, two challenges have hindered systematic cross-kingdom comparisons. First, unlike animal wound healing, the major phases of plant wound healing have not been organized into a universally accepted classification. Second, no comparative framework exists for systematically comparing wound-healing processes across plant and animal kingdoms. To address these challenges, we developed a comparative classification framework that organizes wound healing into three functional phases: (1) bioelectrical signaling, (2) immune responses, and (3) tissue formation and remodeling. This classification defines the major phases of plant wound healing, while the comparative framework establishes a common basis for systematic cross-kingdom comparison. Through comparative analysis, multiple shared cellular and molecular mechanisms were identified. These findings led to a conceptual model termed the hybrid-wound healing system, integrating plant- and animal-derived regenerative responses and providing a theoretical basis for future bioinspired regenerative strategies. Within this system, living plant stem cells are proposed as central biological components that may potentially act as intelligent pharmaceutical microfactories, releasing bioactive molecules in suitable microenvironments. This approach represents a hypothetical future strategy requiring extensive preclinical validation to strategies based on extracts, conditioned media, extracellular vesicles, or isolated bioactive compounds. Collectively, this descriptive review establishes a conceptual foundation for future investigations in plant biology, wound healing, and regenerative medicine.
The cognitive paradigm in medical education is undergoing a transition from traditional knowledge transmission to learner-centered knowledge construction. In China, this shift is aligned with the Outline of the Plan for the Construction of China into an Education Powerhouse (2024-2035), which mandates high-quality, intrinsic development in nursing curricula. While Constructivist Learning Theory (CLT)-based teaching methods (eg, PBL, CBL, and situational simulation) have been widely explored across Chinese nursing institutions, the evidentiary base remains geographically fragmented and methodologically heterogeneous. A systematic synthesis is required to inform national evidence-based educational reforms. This protocol describes a systematic review and meta-analysis designed to evaluate the effectiveness of constructivist learning theory (CLT)-based teaching methods versus traditional lecture-based models on Chinese nursing students' theoretical knowledge, practical skills, self-directed learning ability, and critical thinking disposition. A comprehensive systematic search will be conducted across nine electronic databases: PubMed, Web of Science, the Cochrane Library, Embase, CINAHL, China National Knowledge Infrastructure (CNKI), Wanfang Data, VIP Database (Chinese Scientific and Technological Journal Database), and China Biology Medicine (CBM). The search period spans from database inception to September 27, 2025, with a planned update through June 11, 2026 before final synthesis. Randomized controlled trials and quasi-experimental studies involving Chinese nursing students will be included. Two independent reviewers will screen records, perform full-text assessment, extract data using standardized forms, and code composite CLT interventions, digital or technology-enhanced components, and cluster- or class-based designs using prespecified decision rules. Risk of bias will be assessed using the Cochrane Risk of Bias tool 2 (RoB 2) for randomized trials and the Joanna Briggs Institute (JBI) critical appraisal tools for quasi-experimental studies. Meta-analysis will be performed using Review Manager (RevMan) 5.4 and Stata 18.0, employing random-effects models and prespecified subgroup and sensitivity analyses. This protocol was finalized in February 2026. A preliminary systematic search conducted on September 27, 2025 identified 990 records before deduplication. As of February 6, 2026, deduplication had been completed and title/abstract screening had been initiated. Data extraction, risk-of-bias assessment, and statistical synthesis had not yet started at the protocol stage and will be conducted only after completion of the updated search, final study selection, and full-text eligibility assessment. The final search update was scheduled through June 11, 2026 before data synthesis. The results manuscript will be submitted after completion of all prespecified review steps, with the timeline depending on the number and complexity of newly identified studies. This review will provide a robust evidentiary foundation for the strategic deployment of constructivist methodologies in Chinese nursing education, specifically addressing the needs of vocational and undergraduate programs in the era of digital transformation. https://www.crd.york.ac.uk/PROSPERO/view/CRD420251159499.
Renal ischemia-reperfusion injury (RIRI) is one of the main causes of acute kidney injury (AKI), and its pathological mechanism is complex, mainly involving multiple pathological processes such as oxidative stress outbreak, uncontrolled inflammatory response, abnormal cell apoptosis, and microcirculatory disorders. Currently, there is a lack of efficient and accurate diagnosis and treatment strategies in clinical practice. As a cross discipline integrating nanomaterials, medicine and biology, nanomedicine has shown unique advantages and broad application prospects in the diagnosis and treatment field of RIRI in recent years. Its core carriers include inorganic nanoparticles, polymeric nanoparticles, nanoenzymes, extracellular vesicles, cell membrane camouflage nanoparticles and injectable nanohydrogels. The diagnosis and treatment system based on nanomedicine can breakthrough the limitations of traditional diagnosis and treatment models, and play an important role in early accurate diagnosis, targeted drug delivery, precise treatment of lesion sites, and prolonged drug circulation time in RIRI. It effectively solves pain points such as strong toxicity, short circulation time, and poor targeting of single drugs. However, the specific mechanism of action of nanomedicine in RIRI has not been fully elucidated, and issues such as the biosafety, in vivo metabolic patterns, and clinical translation bottlenecks of nanomedicine still need to be urgently addressed. This review aims to systematically review the application and mechanism research progress of nanomedicine in RIRI, briefly explain the core pathological mechanism of RIRI, focus on the application effects and mechanisms of nanomedicine systems composed of different types of nanocarriers in RIRI diagnosis and treatment, summarize the current research challenges and look forward to future development directions, providing theoretical basis and practical reference for in-depth research, technological breakthroughs, and clinical translation of nanomedicine in the field of RIRI.
Nervonic acid (NA, C24:1 Δ15) is a vital extra-long-chain monounsaturated fatty acid essential for neural development, myelin sheath formation, and neurological health. As the most abundant natural source of NA, Malania oleifera Chun & S.K.Lee has become a key model for studying NA biosynthesis and regulation. This review systematically summarizes the metabolic pathways of nervonic acid biosynthesis in M. oleifera, including plastidial de novo fatty acid synthesis, endoplasmic reticulum (ER)-based very-long-chain fatty acid elongation, and Δ15 desaturation. We focus on the catalytic mechanisms and rate-limiting roles of the elongase complex (KCS, KCR, HCD, ECR) and Δ15 desaturase. Additionally, we integrate recent multi-omics data to analyze key enzyme KCS gene families, their phylogenetic relationships, and syntenic distribution patterns. Furthermore, transcriptional regulatory networks (MYB, bZIP, WRI1, ABI3, FUS3) and epigenetic regulation underlying NA accumulation are also discussed. Finally, we highlight advances, challenges, and prospects in metabolic engineering and synthetic biology for sustainable NA production. This review provides a theoretical basis for the conservation, molecular breeding, and biotechnological utilization of M. oleifera.
Traditional drug discovery is historically characterized by high attrition rates, escalating financial costs, and decades-long development timelines. As global health challenges-particularly antimicrobial resistance and complex malignancies-intensify, the urgent need for innovative and accelerated therapeutic solutions has never been more critical. Artificial Intelligence (AI) has emerged as a supportive computational framework to address these fundamental bottlenecks, offering advanced computational capabilities to navigate vast chemical spaces and optimize molecular design. While AI-based approaches have demonstrated encouraging performance in specific preclinical settings, their practical impact and limitations require careful, objective evaluation. This critical narrative review examines the application of various artificial intelligence technologies in the design and development of antibiotics, anticancer agents, antibodies, and small-molecule drugs, spanning methodologies from conventional machine learning (ML) to advanced deep learning (DL) models. A narrative review of studies reporting applications of artificial intelligence in drug discovery and development. It encompassed articles published between 2000 and 2026 and was informed by literature retrieved from multiple electronic databases. The selected studies focused on AI applications in antibiotics, anticancer agents, antibodies, and small-molecule discovery and development. Studies published before 2000, incomplete reports, or those not directly related to pharmaceutical applications of AI were not considered. Review or meta-analysis articles were also excluded from the primary results, though utilized for background context. Although the inclusion criteria covered studies from 2000 to 2026, one earlier study published before 2000 was also included to provide historical context for the early development of neural network applications in molecular biology. The reviewed literature demonstrates that AI has transitioned from a theoretical concept to a useful framework in early-stage drug discovery, particularly in virtual screening and lead optimization. However, this review identifies a significant "translational gap"; most AI applications remain confined to computational settings, facing challenges in data quality, model interpretability, and a lack of prospective clinical validation. We conclude that while AI significantly accelerates computational efficiency and hypothesis generation, realizing its full potential to combat pressing global health threats requires rigorous experimental integration, standardized data governance, and continuous human expertise to ensure therapeutic efficacy and safety.
Heart failure with preserved ejection fraction (HFpEF) has currently emerged as a predominant and challenging subtype of heart failure, with high morbidity and mortality. However, the efficacy of current therapeutic strategies for HFpEF remains unsatisfactory. The traditional Chinese medicine formulation Shenfu Qiangxin pill (SFQX) ameliorates clinical symptoms in patients with heart failure, but its precise mechanisms for HFpEF remain to be elucidated. This study aimed to investigate the therapeutic potential of SFQX for HFpEF and to elucidate the mechanisms underlying its effects. UPLC-Q-TOF-MS/MS analysis was performed to identify the major active ingredients of SFQX. The HFpEF mouse model was established using a high-fat diet and an Nω-Nitro-L-arginine methyl ester hydrochloride (L-NAME) to evaluate the therapeutic efficacy of SFQX. We employed an integrated approach combining single-cell RNA sequencing (scRNA-seq) with functional and molecular validation to characterize SFQX-induced changes in cardiac cellular composition, cell-state remodeling, and tissue-level phenotypes in HFpEF. We show that SFQX exerts therapeutic effects against HFpEF by coordinately modulating maladaptive cardiac cell subsets. Specifically, SFQX was associated with reprogramming of pathogenic immune cell polarization, normalization of fibroblast state heterogeneity, enhanced endothelial metabolic adaptability, and restoration of lymphatic endothelial homeostasis. These multicellular changes were accompanied by improved cardiac structure and function, reduced fibrosis and inflammation, enhanced lymphatic drainage capacity, and alleviated myocardial edema. Our findings highlight the ability of SFQX, as a multicomponent agent, to precisely regulate the highly heterogeneous pathology of HFpEF at a network level. This work not only establishes a mechanistic link between holistic principles of traditional medicine and modern biology but also provides a novel theoretical basis for SFQX's efficacy in multifactorial diseases.
Gene regulatory networks (GRNs) capture the processes involved in gene regulation. Boolean network (BN) modeling provides a simple but effective framework for understanding the dynamical behavior of GRNs. Although BNs have been widely studied and applied, algorithms and theoretical analyses are usually tested on ad hoc selected or artificially constructed models, which may introduce bias and fail to capture the essential structural and dynamical properties of real GRNs for which they are ultimately intended. Benchmarking offers standardized models for validation and comparison of computational methods and analyses. We construct benchmark BN models for GRNs of four major biological kingdoms: animals, bacteria, fungi, and plants. All models are built from empirically observed recurrent properties and motifs in GRNs. The proposed benchmark BNs provide a systematical and unbiased basis for evaluating algorithms and theoretical analyses.
As an important component of systems biology, omics technologies, with their advantages of high-throughput and holistic analysis, are profoundly transforming the research model of TCM. Pinellia ternata, a commonly used TCM with the effects of drying dampness and resolving phlegm, directing rebellious Qi downward to stop vomiting, and relieving stuffiness and dissipating binds, presents major challenges in quality control and in the elucidation of its complex pharmacological mechanisms. This article systematically reviews the application progress of omics technologies such as genomics, transcriptomics, proteomics, and metabolomics in the study of P. ternata's quality evaluation(covering factors such as cultivation environment, germplasm resources, and processing) and pharmacological mechanisms(including antiemetic effects, phlegm resolution and asthma relief, treatment of gastrointestinal diseases, and antitumor activities). By sorting through existing research findings, it summarizes the great potential of omics technologies in revealing the pharmacodynamic material basis, quality formation mechanisms, and multi-target action networks of P. ternata, and further looks ahead to future research directions such as multi-omics integration, data mining, and artificial intelligence, with a view to providing systematic theoretical and methodological references for the modernization and internationalization of P. ternata research.
Spread through air spaces (STAS) is a recently recognized pattern of invasion in lung cancer that is strongly linked to postoperative recurrence and poor survival. For patients with early-stage disease eligible for sublobar resection, intraoperative identification of STAS on frozen section (FS) could theoretically inform surgical decision-making. However, accumulating evidence, including recent analyses from the JCOG0802/WJOG4607L trial, suggests that STAS positivity portends a poor prognosis regardless of the extent of resection, indicating it is a marker of aggressive systemic biology rather than a purely surgically modifiable risk factor. Therefore, its primary clinical value may lie in postoperative risk stratification, guiding adjuvant strategies, and intensified surveillance rather than reflexive conversion to lobectomy. Yet FS assessment is difficult because of sampling limitations, morphologic heterogeneity, tissue-handling artifacts, and interobserver variability. This narrative review summarizes current knowledge of STAS, including its definition, histologic patterns, biological correlates, and prognostic impact across non-small cell lung cancer subtypes. We then critically appraise data on the feasibility, accuracy, and reproducibility of intraoperative FS for STAS, emphasizing diagnostic pitfalls and recent technical refinements such as optimized tumor-lung interface sampling and lung-inflation techniques. We also discuss emerging roles of artificial intelligence and digital pathology for automated STAS detection, along with radiologic, radiomic, and deep learning approaches for preoperative prediction. Finally, we address controversies surrounding biological versus artifactual STAS and reframe how STAS should influence a multidisciplinary management strategy that extends beyond the immediate choice of surgical extent. We propose practical recommendations for incorporating STAS assessment into multidisciplinary care and highlight future priorities, including prospective STAS-stratified trials, standardized FS workflows, and multimodal prediction models integrating imaging, molecular data, and AI-assisted pathology to support STAS-informed precision risk management.
Lung adenocarcinoma (LUAD), as the most prevalent pathological subtype of lung cancer, is characterized by heterogeneity and therapy resistance that limit clinical efficacy. Histone deacetylation is a key epigenetic mechanism involved in tumorigenesis; however, its spatial distribution and regulatory network in LUAD remain poorly understood. We integrated scRNA-seq, spatial transcriptomics, and molecular biology to investigate histone deacetylase-related genes (HDRGs) in LUAD. Using datasets (GSE131907, GSE189487, TCGA-LUAD), we analyzed HDRG activity, built a 7-gene prognostic model, and validated the FOXA1-HDAC2 axis experimentally. scRNA-seq identified 9 cell types in LUAD microenvironment, with malignant epithelial cells showing high HDRG activity. Spatial analysis revealed HDAC2 and BRD2 co-enrichment in tumor cores, correlated with pyrimidine metabolism. The 7-gene signature demonstrated robust prognostic value across cohorts. High HDAC2 expression correlated with poor prognosis, immune remodeling, and therapy resistance. FOXA1 bound the HDAC2 promoter to enhance its expression; disrupting this axis suppressed tumor progression. HDAC2 knockdown synergized with ERK inhibitor SCH772984 to inhibit tumor growth. This study elucidates the pivotal role of HDRGs in LUAD heterogeneity and malignant progression. The FOXA1-HDAC2 axis is identified as a novel regulatory pathway, providing a theoretical and experimental foundation for prognostic stratification and combination targeted therapy in LUAD.
Non-alcoholic fatty liver disease (NAFLD) is a prevalent metabolic disorder driven by inflammation, in which the NLRP3 inflammasome plays a central role. Current targeted inhibitors face challenges in patient stratification and long-term safety. A narrative literature review was conducted using PubMed, Web of Science, China National Knowledge Infrastructure (CNKI), and Google Scholar from database inception to February 2026. Search terms included combinations of "non-alcoholic fatty liver disease", "NAFLD", "NASH", "NLRP3 inflammasome", "traditional Chinese medicine", "TCM", "herbal medicine", "syndrome differentiation", and "precision medicine". Peer-reviewed original articles, systematic reviews, and clinical studies addressing NLRP3 in NAFLD or TCM-based interventions targeting this pathway were prioritized. TCM syndrome patterns (e.g., damp-heat, phlegm-damp) correlate with distinct NLRP3 activation states and inflammatory phenotypes. Herbal compounds such as berberine and curcumin, as well as classical TCM formulae, exert multi-target effects by suppressing NLRP3 inflammasome assembly, enhancing antioxidant defenses, and modulating the gut-liver axis. Based on this evidence, we propose an integrative "TCM Syndrome-NLRP3 Molecular Endotype-Precision Targeted Therapy" model that links specific TCM syndromes to canonical, non-canonical, or sustained NLRP3 activation, thereby providing hypothesis-driven strategies for personalized intervention. This integrative framework bridges TCM holistic principles with modern inflammasome biology. It provides a theoretical basis for personalized, biomarker-driven NAFLD therapies, highlighting the synergy between traditional medicine and precision hepatology.