Extracellular vesicles (EVs) are pivotal mediators of intercellular communication within the tumor immune microenvironment (TME). They are broadly categorized into exosomes, microvesicles, and apoptotic bodies based on their distinct biogenesis pathways. Exosomes originate from the endosomal system via multivesicular body fusion, microvesicles bud directly from the plasma membrane, and apoptotic bodies are released during programmed cell death. By shuttling diverse bioactive cargoes—including proteins, lipids, and nucleic acids such as mRNA, miRNA, and DNA—EVs exert dual modulatory effects on tumor initiation, progression, and immune evasion. Importantly, EVs exhibit remarkable compositional heterogeneity that is intrinsically linked to their cellular origin. Tumor-derived EVs (TDEVs) are typically enriched with immunosuppressive molecules like PD-L1, TGF‑β, and miR-21, which promote tumor immune escape and metastasis. In contrast, EVs derived from immune cells, such as dendritic cells or cytotoxic T lymphocytes, often carry immunostimulatory components including antigens, co-stimulatory molecules, and granzymes, thereby potentiating anti-tumor immunity. This review systematically delineates the biogenesis and molecular composition of EVs, with a particular emphasis on their dynamic regulatory functions within the TME. Specifically, we discuss how EVs mediate intricate crosstalk between immune and tumor cells, facilitating signal transfer that reshapes immune surveillance. For instance, TDEVs can induce macrophage polarization toward an M2-like pro-tumor phenotype, while also suppressing natural killer cell cytotoxicity and dendritic cell maturation. The clinical utility of EV-associated biomarkers in liquid biopsy is increasingly recognized. Circulating EVs carry tumor-specific molecular signatures that mirror the genetic and proteomic alterations of primary tumors, enabling non-invasive early diagnosis, molecular subtyping, and real-time monitoring of therapeutic responses. Their natural biocompatibility, low immunogenicity, and intrinsic ability to traverse biological barriers make them ideal candidates for drug delivery systems. This review explores cutting-edge applications, including the use of EVs in immune checkpoint blockade therapy—for instance, engineered EVs displaying anti-PD-1 antibodies or carrying siRNA to silence immunosuppressive genes. Moreover, EV-based tumor vaccines are being developed, leveraging dendritic cell-derived EVs loaded with tumor antigens to elicit potent T cell responses. The feasibility of loading EVs with therapeutic molecules such as chemotherapeutic agents, oncolytic viruses, or CRISPR-Cas9 components is also under active investigation. The advent of engineered EVs has further expanded their therapeutic potential. Through surface modification or cargo encapsulation, EVs can be tailored for targeted delivery and controlled release, enhancing precision immunotherapy. However, several hurdles impede clinical translation. Current isolation and purification methods, such as ultracentrifugation and size-exclusion chromatography, suffer from low yield and purity. Distinguishing EV subpopulations remains technically challenging due to overlapping size and marker expression. Moreover, the lack of standardized protocols for EV production, characterization, and quality control poses significant barriers to regulatory approval and clinical adoption. Looking forward, the convergence of multi-omics technologies with artificial intelligence offers a powerful approach to decipher EV heterogeneity and identify robust diagnostic signatures. Machine learning algorithms can integrate proteomic, transcriptomic, and lipidomic data from large patient cohorts to construct predictive models for cancer diagnosis and prognosis. Concurrently, advances in bioengineering are enabling the design of next-generation EVs with enhanced targeting specificity, on-demand drug release, and reduced off-target effects. Future efforts should also focus on establishing good manufacturing practice (GMP)‑compliant production processes and conducting rigorous preclinical and clinical evaluations. In summary, this review provides a comprehensive overview of EV biology, their multifaceted roles in the TME, and their transformative potential in cancer diagnostics and therapeutics. By addressing current challenges and leveraging emerging technologies, EV-based strategies are poised to revolutionize precision oncology.

ObjectiveTo enhance teaching in postoperative cancer rehabilitation, this study developed an integrative Chinese-Western medicine postoperative oncology rehabilitation system, termed the medical oncology generative pre-trained transformer (MedOncoGPT). By introducing MedOncoGPT as an intelligent assistant, an integrated teaching model combining Chinese and Western medicine was established. The study evaluated its impact on students’ integrative clinical reasoning and practical abilities, providing support for instructional reform in related courses. MethodsUsing teaching resources as the knowledge base, MedOncoGPT was built upon the open-source ChatGLM model and incorporated Low-Rank Adaptation (LoRA) fine-tuning and retrieval-augmented generation (RAG) techniques to address postoperative integrative oncology scenarios. The system was applied in courses and clinical clerkships related to integrative oncology. In alignment with course objectives, a five-stage instructional process—pre-class preparation, in-class inquiry, simulated multidisciplinary consultation, clinical reinforcement, and teaching reflection—was designed to guide students in completing syndrome differentiation, comprehensive assessment, and follow-up planning within real or simulated case contexts. Comparative analyses of student engagement, syndrome differentiation thinking, evidence-based awareness, and interdisciplinary integration skills before and after the teaching reform were conducted using questionnaires, course assessments, classroom observations, and semi-structured interviews. ResultsFollowing the implementation of MedOncoGPT, students demonstrated improved performance in case analysis, prescription formulation, and integrative Chinese-Western medical evaluation compared with those receiving traditional instruction. Classroom participation and the relevance of student inquiries also increased. Self-assessment results indicated high levels of satisfaction with respect to clarity of integrative clinical reasoning, ability to retrieve and apply guideline-based evidence, and awareness of appropriate use of intelligent tools in clinical decision-making. More than 92% of students reported that the system facilitated understanding of abstract theoretical concepts presented in textbooks. Instructors noted that the system helped reduce lesson preparation time, enriched typical case materials and discussion scenarios, and promoted the translation of research findings into classroom teaching. Pilot data showed that, with MedOncoGPT assistance, the mean time for initial syndrome differentiation decreased from 18.4 min to 12.1 min, and the agreement rate increased from 68.3% to 82.5%. In the teaching pilot, the experimental group achieved a higher mean score on the final case analysis assessment than the control group (82.6 vs. 74.3). ConclusionThe integration of MedOncoGPT into teaching on postoperative integrative cancer rehabilitation enabled the establishment of a stable instructional process within existing curricula and enhanced students’ integrative clinical reasoning and evidence-based practice capabilities. The approach demonstrates positive potential for advancing the integration of research, clinical practice, and education and represents a valuable exploratory strategy for instructional reform in courses on integrative Chinese-Western medicine.

ObjectiveTo enhance teaching in postoperative cancer rehabilitation, this study developed an integrative Chinese-Western medicine postoperative oncology rehabilitation system, termed the medical oncology generative pre-trained transformer (MedOncoGPT). By introducing MedOncoGPT as an intelligent assistant, an integrated teaching model combining Chinese and Western medicine was established. The study evaluated its impact on students’ integrative clinical reasoning and practical abilities, providing support for instructional reform in related courses. MethodsUsing teaching resources as the knowledge base, MedOncoGPT was built upon the open-source ChatGLM model and incorporated Low-Rank Adaptation (LoRA) fine-tuning and retrieval-augmented generation (RAG) techniques to address postoperative integrative oncology scenarios. The system was applied in courses and clinical clerkships related to integrative oncology. In alignment with course objectives, a five-stage instructional process—pre-class preparation, in-class inquiry, simulated multidisciplinary consultation, clinical reinforcement, and teaching reflection—was designed to guide students in completing syndrome differentiation, comprehensive assessment, and follow-up planning within real or simulated case contexts. Comparative analyses of student engagement, syndrome differentiation thinking, evidence-based awareness, and interdisciplinary integration skills before and after the teaching reform were conducted using questionnaires, course assessments, classroom observations, and semi-structured interviews. ResultsFollowing the implementation of MedOncoGPT, students demonstrated improved performance in case analysis, prescription formulation, and integrative Chinese-Western medical evaluation compared with those receiving traditional instruction. Classroom participation and the relevance of student inquiries also increased. Self-assessment results indicated high levels of satisfaction with respect to clarity of integrative clinical reasoning, ability to retrieve and apply guideline-based evidence, and awareness of appropriate use of intelligent tools in clinical decision-making. More than 92% of students reported that the system facilitated understanding of abstract theoretical concepts presented in textbooks. Instructors noted that the system helped reduce lesson preparation time, enriched typical case materials and discussion scenarios, and promoted the translation of research findings into classroom teaching. Pilot data showed that, with MedOncoGPT assistance, the mean time for initial syndrome differentiation decreased from 18.4 min to 12.1 min, and the agreement rate increased from 68.3% to 82.5%. In the teaching pilot, the experimental group achieved a higher mean score on the final case analysis assessment than the control group (82.6 vs. 74.3). ConclusionThe integration of MedOncoGPT into teaching on postoperative integrative cancer rehabilitation enabled the establishment of a stable instructional process within existing curricula and enhanced students’ integrative clinical reasoning and evidence-based practice capabilities. The approach demonstrates positive potential for advancing the integration of research, clinical practice, and education and represents a valuable exploratory strategy for instructional reform in courses on integrative Chinese-Western medicine.

Adipose tissue macrophages (ATMs) are crucial immunomodulatory factors in the adipose tissue (AT) microenvironment, playing an irreplaceable role in maintaining the balance of the local immune system and regulating metabolic homeostasis. Under obese conditions, the excessive accumulation of lipids leads to abnormal expansion of adipose tissue, which further disrupts the homeostasis of the local microenvironment, including the imbalance of inflammatory factors, the occurrence of oxidative stress, and the damage of microcirculation. As an essential immune cell population in adipose tissue, ATMs are deeply involved in the occurrence and progression of metabolic disorders and multiple obesity-related diseases, and their functional abnormalities are closely related to the initiation and development of adipose tissue inflammation and systemic metabolic disorders. The interaction between ATMs and adipokines, including adiponectin, leptin, resistin and retinol-binding protein 4 (RBP4), acts as the core immunological and molecular mechanism linking adipose tissue inflammation to continuous disease deterioration, and this interaction is also a key research focus in the field of obesity-related diseases in recent years. This review discusses in detail the facilitating effect of the AT inflammatory microenvironment on the differentiation of peripheral monocytes into ATMs, elaborating on the specific molecular mechanisms by which various inflammatory mediators and abnormal metabolic products in the inflammatory microenvironment induce the differentiation of peripheral monocytes into functional ATMs. It also focuses on the regulatory mechanisms of chemokine-mediated ATM polarization and recruitment, including the specific roles of key chemokines like monocyte chemoattractant protein‑1 (MCP-1) and C‑X‑C motif chemokine ligand 10 (CXCL10) in mediating the recruitment of ATMs to adipose tissue, as well as the molecular pathways that regulate the switch between M1 pro-inflammatory phenotype and M2 anti-inflammatory phenotype of ATMs. In addition, this review explores the specific process by which ATMs trigger AT inflammation by secreting various pro-inflammatory factors such as tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), and IL-1β, which further amplify the local inflammatory response and disrupt the metabolic homeostasis of adipose tissue. Furthermore, it analyzes the specific binding patterns and molecular characteristics of adiponectin, leptin, resistin and RBP4 with ATM surface receptors, and clarifies how the activation of downstream immune signaling pathways, such as Janus kinase‑signal transducer and activator of transcription (JAK-STAT), nuclear factor‑κB (NF‑κB), and phosphatidylinositol 3‑kinase‑protein kinase B (PI3K-AKT), triggered by these binding processes, induces the occurrence and development of various obesity-related diseases, including insulin resistance, cancer, metabolic-associated steatohepatitis (MASH) and cardiovascular diseases. This paper highlights the unique and crucial role of the interaction between ATMs and adipokines in the occurrence and progression of obesity-related diseases, emphasizing that this interaction is a key link connecting local adipose tissue inflammation to systemic metabolic disorders. It further summarizes the potential therapeutic strategies targeting the ATM-adipokine axis, and comprehensively illustrates three major clinical application barriers, namely biological complexity, technical bottlenecks, and clinical translation obstacles, in the process of applying these therapeutic strategies. For each barrier, this review puts forward corresponding feasible solutions and clear research directions, which not only enrich the theoretical system of the ATM-adipokine axis in the field of obesity research, but also provide a solid theoretical basis and practical ideas for the clinical intervention, prevention and treatment of obesity and its related diseases.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective To investigate the causal relationship between cathepsin(CTS)and thyroid cancer by using Mendelian randomization(MR).Methods Aggregate data for CTS GWAS was obtained from the IEU Open Genome-wide Association Study(GWAS)database(https://gwas.mrcieu.ac.uk/),and thyroid cancer GWAS data was obtained from the EBI database(https://www.ebi.ac.uk/).Five different MR analysis methods were utilized,with the inverse variance-weighted method(IVW)as the main approach,complemented by the weighted median method,MR-Egger regression,simple mode method and weighted majority method.These methods were employed to analyze the relationship between 9 CTS genes and thyroid cancer through 9 double-sample analyses.MR-Egger intercept,MR-PRESSO for gene pleiotropy detection,Cochran Q test,and leave-one-out method were applied to assess pleiotropy and sensitivity,followed by reverse MR analysis.Results MR analysis showed that elevated level of CTS B was positively correlated with the risk of thyroid cancer(IVW OR=1.60,95%CI 1.12-2.30,P=0.01),while elevated CTS O level was negatively correlated with the risk of thyroid cancer(IVW OR=0.65,95%CI 0.45-0.95,P=0.02).Reverse MR analysis revealed no significant causal relationship between thyroid cancer and CTS B and CTS O(P>0.05).Conclusion CTS B may promote the development of thyroid cancer,whereas CTS O may inhibit its progression.

Neuronal reprogramming is an innovative technique for converting non-neuronal somatic cells into neurons that can be used to replace lost or damaged neurons, providing a potential effective therapeutic strategy for central nervous system (CNS) injuries or diseases. Transcription factors have been used to induce neuronal reprogramming, while their reprogramming efficiency is relatively low, and the introduction of exogenous genes may result in host gene instability or induce gene mutation. Therefore, their future clinical application may be hindered by these safety concerns. Compared with transcription factors, small-molecule compounds have unique advantages in the field of neuronal reprogramming, which can overcome many limitations of traditional transcription factor-induced neuronal reprogramming. Here, we review the recent progress in the research of small-molecule compound-mediated neuronal reprogramming and its application in CNS regeneration and repair.
Humans ; Cellular Reprogramming/drug effects* ; Neurons/cytology* ; Animals ; Transcription Factors ; Small Molecule Libraries/pharmacology* ; Nerve Regeneration

The circadian clock plays a critical role in regulating various physiological processes, including gene expression, metabolic regulation, immune response, and the sleep-wake cycle in living organisms. Post-translational modifications (PTMs) are crucial regulatory mechanisms to maintain the precise oscillation of the circadian clock. By modulating the stability, activity, cell localization and protein-protein interactions of core clock proteins, PTMs enable these proteins to respond dynamically to environmental and intracellular changes, thereby sustaining the periodic oscillations of the circadian clock. Different types of PTMs exert their effects through distincting molecular mechanisms, collectively ensuring the proper function of the circadian system. This review systematically summarized several major types of PTMs, including phosphorylation, acetylation, ubiquitination, SUMOylation and oxidative modification, and overviewed their roles in regulating the core clock proteins and the associated pathways, with the goals of providing a theoretical foundation for the deeper understanding of clock mechanisms and the treatment of diseases associated with circadian disruption.
Protein Processing, Post-Translational/physiology* ; Circadian Rhythm/physiology* ; Humans ; Animals ; CLOCK Proteins/physiology* ; Circadian Clocks/physiology* ; Phosphorylation ; Acetylation ; Ubiquitination ; Sumoylation