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.

ObjectiveGastric hemorrhage is one of the most common and life-threatening emergencies of the upper digestive tract. Early identification and continuous monitoring are essential for reducing rebleeding rates and mortality, particularly within the critical early hours after onset. Although endoscopy and radiological imaging can accurately localize bleeding sites, these approaches are invasive, resource-intensive, and unsuitable for continuous bedside monitoring. Electrical impedance tomography (EIT), as a noninvasive and radiation-free functional imaging technique, offers real-time visualization of conductivity distribution and has the potential for detecting intragastric bleeding based on the electrical contrast between blood and surrounding gastric tissues. In this study, a three-dimensional gastric EIT (3D-gEIT) framework is proposed to achieve noninvasive, real-time, and dynamic monitoring of gastric hemorrhage, with emphasis on spatial localization and quantitative volume assessment. MethodsA three-dimensional upper-abdominal simulation model incorporating the stomach, gastric wall, gastric contents, and surrounding tissues was established. Three electrode configurations, namely the dual layer ring, the four layer staggered ring, and the opposed dual plane array, were designed and systematically compared to evaluate their influence on depth sensitivity and spatial resolution. Based on the Tikhonov-Noser hybrid regularization scheme, a region-clustering constraint was introduced to develop the TK-Noser-RCC algorithm. This approach aggregates spatially adjacent elements with similar conductivity variations, thereby enhancing structural continuity and suppressing isolated noise artifacts. To validate the proposed framework, an upper-abdominal physical phantom was constructed using agar to simulate background tissue conductivity. Hemispherical high-conductivity inclusions with volumes ranging from 10 ml to 50 ml were attached to the inner gastric wall to mimic localized bleeding under different gastric filling states. Boundary voltages were acquired under a 120 kHz excitation current and reconstructed using the TK-Noser-RCC algorithm. Furthermore, an in vivo animal experiment was performed using a porcine model with adult-scale abdominal dimensions. A total of 100 ml of autologous blood was injected incrementally into the stomach to simulate progressive gastric hemorrhage, and time-difference EIT reconstruction was conducted at each injection stage to assess the dynamic system response under physiological conditions. ResultsSimulation results demonstrated that the opposed dual-plane electrode array achieved superior depth sensitivity distribution and spatial resolution. For a 40 ml hemorrhage model, the average ICC and SSIM improved by 55.9% and 38.8% compared with the dual-layer ring configuration, and by 64.0% and 39.5% compared with the four-layer staggered configuration. The proposed region-clustering constraint significantly enhanced reconstruction stability. Under added Gaussian noise of 40 dB and 30 dB, ICC values remained approximately 0.85, indicating effective artifact suppression and preservation of boundary integrity. In physical phantom experiments, reconstructed hemorrhage volumes increased approximately linearly with the preset hemispherical volumes, and the reconstructed high-conductivity regions closely matched the actual bleeding locations. Both empty-stomach and full-stomach conditions were evaluated, demonstrating that the opposed dual-plane configuration maintained stable imaging performance across varying gastric contents. In the animal experiment, reconstructed low-impedance regions expanded progressively with increasing injected blood volume. The spatial localization of the hemorrhage remained stable throughout the procedure, and no significant artifacts were observed. Quantitative analysis showed that reconstructed volume and average conductivity variation exhibited an approximately linear growth trend with injected blood volume, confirming the sensitivity of the system to dynamic intragastric conductivity changes. ConclusionThe proposed 3D-gEIT framework enables quantitative reconstruction of gastric hemorrhage volume and spatial distribution with improved depth sensitivity, structural continuity, and noise robustness compared with conventional EIT approaches. By integrating optimized electrode configuration and a region-clustering-constrained reconstruction algorithm, the system provides stable dynamic monitoring under both controlled phantom conditions and in vivo physiological environments. This method offers a noninvasive, real-time, and low-cost imaging strategy for early diagnosis, postoperative monitoring, and bedside surveillance of gastric bleeding.

ObjectiveThis study aims to explore the potential of different orders of magnitude single-nucleotide polymorphism (SNP) locus combinations for predicting distant kinship relationships. A high-density SNP locus set was constructed, and a comprehensive assessment of its inference capability was conducted. MethodsFirstly, we selected three commercial chip panels, CGA (Chinese genotyping array, Illumina), GSA (Global screening array, Illumina), Affy (23MF_V2 high-density SNP array, Affymetrix) and merged them after quality control, forming a high-density SNP locus panel(1 180 k). Secondly, we selected 161 samples and collected their peripheral blood samples by using whole-genome sequencing technology. Within this sample population, the levels of kinship relationships fully covered the range from level 1 to level 9, and the number of kinship pairs at each level was consistently maintained at over 50 pairs. From 161 samples data of whole-genome sequencing, the 1 180 k locus set was extracted, which is referred to as the high-density SNP locus set in the following text. The kinship inference was conducted using the identity-by-descent (IBD) algorithm with the selected optimal parameters. To comprehensively evaluate the performance of the high-density SNP locus set in kinship inference, we compared it with the three commercial chip panels, the intersection of these three chip loci, and the control sets constructed by randomly reducing the number of the high-density SNP locus set. Based on the changes in the IBD lengths, as well as the dynamic trends in prediction accuracy, we conducted a scientific assessment of the kinship inference capability of the high-density SNP locus set. ResultsAfter screening, a set of 1 184 334 autosomal SNPs was obtained. During the process of screening the optimal IBD length threshold, the result revealed that 0 cM, 1 cM, and 2 cM all demonstrated good applicability. However, to avoid the issue of a large amount of redundant information caused by setting a too low IBD length threshold, this study ultimately selected 2 cM as the optimal threshold. Compared with the average results of three chip panels, the high-density SNP locus set increased the total IBD length and the average IBD length across levels 1-9; the accuracy of the confidence interval for level 8 was 70.97%, which represented a 3.50% improvement; the average confidence interval accuracy for levels 1-8 was 91.39%, representing a 1.00% increase; and the false negative rates at levels 8 and 9 were reduced by 2.42% and 6.76%, respectively. The system efficacy of the high-density SNP locus set for kinship inference of first to eighth degree relationships reached 98.91%. Through random reduction of the high-density SNP locus set results, it is found that increasing the number of SNPs with the panel, the detection efficiency of IBD length showed a significant upward trend. At the same time, the overall trend in the accuracy of kinship relationship prediction as well as the confidence interval accuracy also indicated that both metrics steadily increased with the addition of more loci. ConclusionThe results show that the high-density SNPs panel significantly enhances the efficacy of distant kinship inference, accurately covering kinship degrees, with the average confidence interval accuracy for first to eighth degree relationships stably above 90%. The study finds that increasing the number of SNPs panel can improve the ability to predict distant kinship.

Depression, a prevalent mental disorder with significant socioeconomic burdens, underscores the urgent need for safe and effective non-pharmacological interventions. Recent advances in microbiome research have revealed the pivotal role of gut microbiota dysbiosis in the pathogenesis of depression. Concurrently, exercise, as a cost-effective and accessible intervention, has demonstrated remarkable efficacy in alleviating depressive symptoms. This comprehensive review synthesizes current evidence on the interplay among exercise, gut microbiota modulation, and depression, elucidating the mechanistic pathways through which exercise ameliorates depressive symptoms via the microbiota-gut-brain (MGB) axis. Depression is characterized by gut microbiota alterations, including reduced alpha and beta diversity, depletion of beneficial taxa (e.g., Bifidobacterium, Lactobacillus, and Coprococcus), and overgrowth of pro-inflammatory and pathogenic bacteria (e.g., Morganella, Klebsiella, and Enterobacteriaceae). Metagenomic analyses reveal disrupted metabolic functions in depressive patients, such as diminished synthesis of short-chain fatty acids (SCFAs), impaired tryptophan metabolism, and dysregulated bile acid conversion. For instance, Bifidobacterium longum deficiency correlates with reduced synthesis of neuroactive metabolites like homovanillic acid, while decreased Coprococcus abundance limits butyrate production, exacerbating neuroinflammation. Furthermore, elevated levels of indole derivatives from Clostridium species inhibit serotonin (5-HT) synthesis, contributing to depressive phenotypes. These dysbiotic profiles disrupt the MGB axis, triggering systemic inflammation, neurotransmitter imbalances, and hypothalamic-pituitary-adrenal (HPA) axis hyperactivity. Exercise exerts profound effects on gut microbiota composition, diversity, and metabolic activity. Longitudinal studies demonstrate that sustained aerobic exercise increases alpha diversity, enriches SCFA-producing genera (e.g., Faecalibacterium prausnitzii, Roseburia, and Akkermansia), and suppresses pathobionts (e.g., Desulfovibrio and Streptococcus). For example, a meta-analysis of 25 trials involving 1 044 participants confirmed that exercise enhances microbial richness and restores the Firmicutes/Bacteroidetes ratio, a biomarker of metabolic health. Notably, endurance training promotes Veillonella proliferation, which converts lactate into propionate, enhancing energy metabolism and delaying fatigue. Exercise also strengthens intestinal barrier integrity by upregulating tight junction proteins (e.g., ZO-1, occludin), thereby reducing lipopolysaccharide (LPS) translocation and systemic inflammation. However, excessive exercise may paradoxically diminish microbial diversity and exacerbate intestinal permeability, highlighting the importance of moderate intensity and duration. Exercise ameliorates depressive symptoms through multifaceted interactions with the gut microbiota, primarily via 4 interconnected pathways. First, exercise mitigates neuroinflammation by elevating anti-inflammatory SCFAs such as butyrate, which suppresses NF-κB signaling to attenuate microglial activation and oxidative stress in the hippocampus. Animal studies demonstrate that voluntary wheel running reduces hippocampal TNF‑α and IL-17 levels in stress-induced depression models, while fecal microbiota transplantation (FMT) from exercised mice reverses depressive behaviors by modulating the TLR4/NF‑κB pathway. Second, exercise regulates neurotransmitter dynamics by enriching GABA-producing Lactobacillus and Bifidobacterium, thereby counteracting neuronal hyperexcitability. Aerobic exercise also enhances the abundance of Lactobacillus plantarum and Streptococcus thermophilus, which facilitate 5-HT and dopamine synthesis. Clinical trials reveal that 12 weeks of moderate exercise increases fecal Coprococcus and Blautia abundance, correlating with improved 5-HT bioavailability and reduced depression scores. Third, exercise normalizes HPA axis hyperactivity by reducing cortisol levels and restoring glucocorticoid receptor sensitivity. In rodent models, chronic stress-induced corticosterone elevation is reversed by probiotic supplementation (e.g., Lactobacillus), which enhances endocannabinoid signaling and hippocampal neurogenesis. Furthermore, exercise upregulates brain-derived neurotrophic factor (BDNF) via microbial metabolites like butyrate, promoting histone acetylation and synaptic plasticity. FMT experiments confirm that exercise-induced microbiota elevates prefrontal BDNF expression, reversing stress-induced neuronal atrophy. Fourth, exercise reshapes microbial metabolic crosstalk, diverting tryptophan metabolism toward 5-HT synthesis instead of neurotoxic kynurenine derivatives. Butyrate inhibits indoleamine 2,3-dioxygenase (IDO), a key enzyme in the kynurenine pathway linked to depression. Concurrently, exercise-induced Akkermansia enrichment enhances mucin production, fortifies the gut barrier, and reduces LPS-driven neuroinflammation. Collectively, these mechanisms underscore exercise as a potent modulator of the microbiota-gut-brain axis, offering a holistic approach to alleviating depression through microbial and neurophysiological synergy. Current evidence supports exercise as a potent adjunct therapy for depression, with personalized regimens (e.g., aerobic, resistance, or yoga) tailored to individual microbiota profiles. However, challenges remain in optimizing exercise prescriptions (intensity, duration, and type) and integrating them with probiotics, prebiotics, or FMT for synergistic effects. Future research should prioritize large-scale randomized controlled trials to validate causality, multi-omics approaches to decipher MGB axis dynamics, and mechanistic studies exploring microbial metabolites as therapeutic targets. The authors advocate for a paradigm shift toward microbiota-centric interventions, emphasizing the bidirectional relationship between physical activity and gut ecosystem resilience in mental health management. In conclusion, this review underscores exercise as a multifaceted modulator of the gut-brain axis, offering novel insights into non-pharmacological strategies for depression. By bridging microbial ecology, neuroimmunology, and exercise physiology, this work lays a foundation for precision medicine approaches targeting the gut microbiota to alleviate depressive disorders.

Fibrosis, the pathological scarring of vital organs, is a severe and often irreversible condition that leads to progressive organ dysfunction. It is particularly pronounced in organs like the liver, kidneys, lungs, and heart. Despite its clinical significance, the full understanding of its etiology and complex pathogenesis remains incomplete, posing substantial challenges to diagnosing, treating, and preventing the progression of fibrosis. Among the various molecular players involved, platelet-derived growth factor-C (PDGF-C) has emerged as a crucial factor in fibrotic diseases, contributing to the pathological transformation of tissues in several key organs. PDGF-C is a member of the PDGFs family of growth factors and is synthesized and secreted by various cell types, including fibroblasts, smooth muscle cells, and endothelial cells. It acts through both autocrine and paracrine mechanisms, exerting its biological effects by binding to and activating the PDGF receptors (PDGFRs), specifically PDGFRα and PDGFRβ. This binding triggers multiple intracellular signaling pathways, such as JAK/STAT, PI3K/AKT and Ras-MAPK pathways. which are integral to the regulation of cell proliferation, survival, migration, and fibrosis. Notably, PDGF-C has been shown to promote the proliferation and migration of fibroblasts, key effector cells in the fibrotic process, thus accelerating the accumulation of extracellular matrix components and the formation of fibrotic tissue. Numerous studies have documented an upregulation of PDGF-C expression in various fibrotic diseases, suggesting its significant role in the initiation and progression of fibrosis. For instance, in liver fibrosis, PDGF-C stimulates hepatic stellate cell activation, contributing to the excessive deposition of collagen and other extracellular matrix proteins. Similarly, in pulmonary fibrosis, PDGF-C enhances the migration of fibroblasts into the damaged areas of lungs, thereby worsening the pathological process. Such findings highlight the pivotal role of PDGF-C in fibrotic diseases and underscore its potential as a therapeutic target for these conditions. Given its central role in the pathogenesis of fibrosis, PDGF-C has become an attractive target for therapeutic intervention. Several studies have focused on developing inhibitors that block the PDGF-C/PDGFR signaling pathway. These inhibitors aim to reduce fibroblast activation, prevent the excessive accumulation of extracellular matrix components, and halt the progression of fibrosis. Preclinical studies have demonstrated the efficacy of such inhibitors in animal models of liver, kidney, and lung fibrosis, with promising results in reducing fibrotic lesions and improving organ function. Furthermore, several clinical inhibitors, such as Olaratumab and Seralutinib, are ongoing to assess the safety and efficacy of these inhibitors in human patients, offering hope for novel therapeutic options in the treatment of fibrotic diseases. In conclusion, PDGF-C plays a critical role in the development and progression of fibrosis in vital organs. Its ability to regulate fibroblast activity and influence key signaling pathways makes it a promising target for therapeutic strategies aiming at combating fibrosis. Ongoing research into the regulation of PDGF-C expression and the development of PDGF-C/PDGFR inhibitors holds the potential to offer new insights and approaches for the diagnosis, treatment, and prevention of fibrotic diseases. Ultimately, these efforts may lead to the development of more effective and targeted therapies that can mitigate the impact of fibrosis and improve patient outcomes.

Membrane proteins are integral components of cellular membranes, accounting for approximately 30% of the mammalian proteome and serving as targets for 60% of FDA-approved drugs. They are critical to both physiological functions and disease mechanisms. Their functional protein-protein interactions form the basis for many physiological processes, such as signal transduction, material transport, and cell communication. Membrane protein interactions are characterized by membrane environment dependence, spatial asymmetry, weak interaction strength, high dynamics, and a variety of interaction sites. Therefore, in situ analysis is essential for revealing the structural basis and kinetics of these proteins. This paper introduces currently available in situ analytical techniques for studying membrane protein interactions and evaluates the characteristics of each. These techniques are divided into two categories: label-based techniques (e.g., co-immunoprecipitation, proximity ligation assay, bimolecular fluorescence complementation, resonance energy transfer, and proximity labeling) and label-free techniques (e.g., cryo-electron tomography, in situ cross-linking mass spectrometry, Raman spectroscopy, electron paramagnetic resonance, nuclear magnetic resonance, and structure prediction tools). Each technique is critically assessed in terms of its historical development, strengths, and limitations. Based on the authors’ relevant research, the paper further discusses the key issues and trends in the application of these techniques, providing valuable references for the field of membrane protein research. Label-based techniques rely on molecular tags or antibodies to detect proximity or interactions, offering high specificity and adaptability for dynamic studies. For instance, proximity ligation assay combines the specificity of antibodies with the sensitivity of PCR amplification, while proximity labeling enables spatial mapping of interactomes. Conversely, label-free techniques, such as cryo-electron tomography, provide near-native structural insights, and Raman spectroscopy directly probes molecular interactions without perturbing the membrane environment. Despite advancements, these methods face several universal challenges: (1) indirect detection, relying on proximity or tagged proxies rather than direct interaction measurement; (2) limited capacity for continuous dynamic monitoring in live cells; and (3) potential artificial influences introduced by labeling or sample preparation, which may alter native conformations. Emerging trends emphasize the multimodal integration of complementary techniques to overcome individual limitations. For example, combining in situ cross-linking mass spectrometry with proximity labeling enhances both spatial resolution and interaction coverage, enabling high-throughput subcellular interactome mapping. Similarly, coupling fluorescence resonance energy transfer with nuclear magnetic resonance and artificial intelligence (AI) simulations integrates dynamic structural data, atomic-level details, and predictive modeling for holistic insights. Advances in AI, exemplified by AlphaFold’s ability to predict interaction interfaces, further augment experimental data, accelerating structure-function analyses. Future developments in cryo-electron microscopy, super-resolution imaging, and machine learning are poised to refine spatiotemporal resolution and scalability. In conclusion, in situ analysis of membrane protein interactions remains indispensable for deciphering their roles in health and disease. While current technologies have significantly advanced our understanding, persistent gaps highlight the need for innovative, integrative approaches. By synergizing experimental and computational tools, researchers can achieve multiscale, real-time, and perturbation-free analyses, ultimately unraveling the dynamic complexity of membrane protein networks and driving therapeutic discovery.

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

The study was conducted to investigate the mechanism of Xinyang Tablets( XYP) in modulating the fat mass and obesity-associated protein(FTO)/N6-methyladenosine(m6A) signaling pathway to ameliorate ventricular remodeling in heart failure(HF). A mouse model of HF was established by transverse aortic constriction(TAC). Mice were randomized into sham, model, XYP(low, medium, and high doses), and positive control( perindopril) groups(n= 10). From day 3 post-surgery, mice were administrated with corresponding drugs by gavage for 6 consecutive weeks. Following the treatment, echocardiography was employed to evaluate the cardiac function, and RT-qPCR was employed to determine the relative m RNA levels of key markers, including atrial natriuretic peptide( ANP), B-type natriuretic peptide( BNP), β-myosin heavy chain(β-MHC), collagen type I alpha chain(Col1α), collagen type Ⅲ alpha chain(Col3α), alpha smooth muscle actin(α-SMA), and FTO. The cardiac tissue was stained with Masson's trichrome and wheat germ agglutinin(WGA) to reveal the pathological changes. Immunohistochemistry was employed to detect the expression levels of Col1α, Col3α, α-SMA, and FTO in the myocardial tissue. The m6A modification level in the myocardial tissue was measured by the m6A assay kit. An H9c2 cell model of cardiomyocyte injury was induced by angiotensin Ⅱ(AngⅡ), and small interfering RNA(siRNA) was employed to knock down FTO expression. RT-qPCR was conducted to assess the relative m RNA levels of FTO and other genes associated with cardiac remodeling. The m6A modification level was measured by the m6A assay kit, and Western blot was employed to determine the phosphorylated phosphatidylinositol 3-kinase(p-PI3K)/phosphatidylinositol 3-kinase(PI3K) and phosphorylated serine/threonine kinase(p-Akt)/serine/threonine kinase(Akt) ratios in cardiomyocytes. The results of animal experiments showed that the XYP treatment significantly improved the cardiac function, reduced fibrosis, up-regulated the m RNA and protein levels of FTO, and lowered the m6A modification level compared with the model group. The results of cell experiments showed that the XYP-containing serum markedly up-regulated the m RNA level of FTO while decreasing the m6A modification level and the p-PI3K/PI3K and p-Akt/Akt ratios in cardiomyocytes. Furthermore, FTO knockdown reversed the protective effects of XYP-containing serum on Ang Ⅱ-induced cardiomyocyte hypertrophy. In conclusion, XYP may ameliorate ventricular remodeling by regulating the FTO/m6A axis, thereby inhibiting the activation of the PI3K/Akt signaling pathway.
Animals ; Ventricular Remodeling/drug effects* ; Heart Failure/physiopathology* ; Signal Transduction/drug effects* ; Mice ; Male ; Alpha-Ketoglutarate-Dependent Dioxygenase FTO/genetics* ; Drugs, Chinese Herbal/administration & dosage* ; Mice, Inbred C57BL ; Humans ; Adenosine/analogs & derivatives* ; Myocytes, Cardiac/metabolism* ; Disease Models, Animal

Cardiac fibrosis(CF) is a cardiac pathological process characterized by excessive deposition of extracellular matrix(ECM). When the heart is damaged by adverse stimuli, cardiac fibroblasts are activated and secrete a large amount of ECM, leading to changes in cardiac fibrosis, myocardial stiffness, and cardiac function declines and accelerating the development of heart failure. There is a close relationship between heart yin deficiency and cardiac fibrosis, which have similar pathogenic mechanisms. Heart Yin deficiency, characterized by insufficient Yin fluids, causes the heart to lose its nourishing function, which acts as the initiating factor for myocardial dystrophy. The deficiency of body fluids leads to stagnation of blood flow, resulting in blood stasis and water retention. Blood stasis and water retention accumulate in the heart, which aligns with the pathological manifestation of excessive deposition of ECM, as a tangible pathogenic factor. This is an inevitable stage of the disease process. The lingering of blood stasis combined with water retention eventually leads to the generation of heat and toxins, triggering inflammatory responses similar to heat toxins, which continuously stimulate the heart and cause the ultimate outcome of CF. Considering the syndrome of heart Yin deficiency, traditional Chinese medicine capable of nourishing Yin, activating blood, and promoting urination can reduce myocardial cell apoptosis, inhibit fibroblast activation, and lower the inflammation level, showing significant advantages in combating CF.
Humans ; Fibrosis/drug therapy* ; Animals ; Yin Deficiency/metabolism* ; Myocardium/metabolism* ; Medicine, Chinese Traditional ; Drugs, Chinese Herbal/therapeutic use*