ObjectiveThe exacerbating trend of global population aging poses profound socioeconomic and public health challenges, making the comprehensive elucidation of biological aging mechanisms and the discovery of effective anti-aging interventions an urgent priority in the life sciences. Based on our previous serum metabolomics findings that dimethylglycine, an intermediate metabolite of amino acid metabolism naturally present in the human body, was significantly enriched in the serum of longevity families, this study aimed to systematically investigate the anti-aging effects of dimethylglycine both in living organisms and in controlled laboratory environments, and to preliminarily elucidate its underlying molecular mechanisms. While existing literature indicates that dimethylglycine possesses antioxidant and immunomodulatory properties, its direct anti-aging efficacy and the specific molecular pathways through which it operates remain largely unexplored. MethodsTo comprehensively evaluate the anti-aging properties of dimethylglycine, we utilized replicative senescent human embryonic lung fibroblasts, specifically the WI-38 cell line, as an experimental model in a controlled laboratory environment. Cell viability and safety were thoroughly assessed using Cell Counting Kit-8 and lactate dehydrogenase release assays across various concentrations of dimethylglycine. The impact of dimethylglycine on cellular senescence phenotypes, oxidative stress, and proliferative capacity was evaluated via senescence-associated beta-galactosidase staining, reactive oxygen species fluorescence detection, and 5-ethynyl-2'-deoxyuridine incorporation assays. Furthermore, the molecular alterations of senescence-associated secretory phenotype factors and core senescence signaling pathways were quantified using quantitative reverse transcription polymerase chain reaction for the messenger RNA levels of interleukin-6, interleukin-8, p21, and matrix metalloproteinase-1, and enzyme-linked immunosorbent assay for the measurement of p16 and p21 protein expression levels. For the living organism model, the wild-type nematode Caenorhabditis elegans was used to evaluate systemic physiological effects. We conducted a comprehensive lifespan analysis at 20°C, heat stress resistance survival assays at 35℃, senescence-associated beta-galactosidase staining, lipofuscin accumulation tracking, intracellular reactive oxygen species measurement, and Oil Red O staining to ascertain systemic lipid accumulation. Additionally, network pharmacology bioinformatics tools, including PharmMapper and STRING databases, and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were utilized to predict target pathways, alongside highly detailed molecular docking simulations utilizing SwissDock and Protein-Ligand Interaction Profiler to examine interactions with the cytochrome P450 family 2 subfamily C member 9 protein. ResultsThe experimental outcomes robustly demonstrate the potent anti-aging capabilities of dimethylglycine. At the cellular level, toxicity analyses firmly confirmed that dimethylglycine is highly safe; continuous treatment with 50 mol/L and 70 mol/L of dimethylglycine for 5 d did not induce any cellular membrane damage or cytotoxicity, but rather actively promoted cellular proliferation. Utilizing the optimal standardized concentration of 50 mol/L, dimethylglycine treatment significantly ameliorated senescent phenotypic markers in human embryonic lung fibroblasts, which was evidenced by a drastic and highly significant reduction in the senescence-associated beta-galactosidase positive cell percentage (P<0.000 1) and intracellular reactive oxygen species levels (P<0.000 1), alongside a marked increase in the 5-ethynyl-2'-deoxyuridine-positive proliferation rate (P=0.003 5). On a molecular expression scale, dimethylglycine significantly downregulated the messenger RNA expression of multiple core senescence-associated secretory phenotype inflammatory factors, including interleukin-6, interleukin-8, p21, and matrix metalloproteinase-1. Concurrently, it effectively suppressed the protein expression of critical cell cycle arrest markers, diminishing p16 protein levels by 57.3% (P=0.000 4) and p21 protein levels by 27.2% (P=0.000 7). In the nematode Caenorhabditis elegans animal model, dimethylglycine significantly extended the mean lifespan from 20.402 d to an impressive 23.066 d (P<0.000 1) and notably enhanced overall survival rates under severe heat stress environmental conditions (P=0.017). Furthermore, systemic dimethylglycine intervention significantly mitigated age-related physiological decline by decreasing bodily lipofuscin accumulation (P<0.000 1), significantly reducing senescence-associated beta-galactosidase activity, lowering systemic reactive oxygen species fluorescence (P=0.008), and effectively alleviating overall fat accumulation (P<0.000 1). Mechanistically, extensive network pharmacology and Kyoto Encyclopedia of Genes and Genomes analyses strongly revealed that the potential targets of dimethylglycine are significantly enriched in fundamental drug metabolism and oxidative stress response pathways. Precision molecular docking simulations conclusively demonstrated that dimethylglycine forms highly stable structural interactions with the cytochrome P450 family 2 subfamily C member 9 protein, specifically highlighting the definitive formation of 5 stable hydrogen bonds involving serine 365, leucine 366, and serine 429 residues, as well as two critical salt bridge formations with arginine 97 and histidine 368 residues. It is additionally predicted to interact favorably with glutathione S-transferase family proteins. ConclusionDimethylglycine exhibits a profoundly significant and multifaceted anti-aging activity at both the cellular and entire living animal levels. By powerfully alleviating oxidative stress, heavily suppressing the core p16 and p21-dependent cellular senescence signaling pathways, and substantially mitigating the detrimental senescence-associated secretory phenotype, dimethylglycine effectively delays fundamental cellular senescence processes and drastically extends whole-organism lifespan. The biological mechanisms driving these robust protective effects are highly likely closely associated with its direct stable interactions with crucial metabolic and detoxifying enzyme systems, such as cytochrome P450 family 2 subfamily C member 9 and glutathione S-transferase family proteins, thereby systemically improving metabolic dysregulation and restoring critical redox homeostasis. This comprehensive study provides highly solid experimental evidence supporting dimethylglycine as a highly potent and safe potential anti-aging intervention agent, while simultaneously offering a clear molecular mechanistic explanation for the previously documented high abundance of dimethylglycine observed within exceptionally long-lived human populations.

Bone metastases are a common and serious complication of solid tumors such as breast cancer, prostate cancer, lung cancer, and kidney cancer, and occur at a high rate in patients with advanced cancer. This pathological process not only frequently leads to skeletal-related events such as severe bone pain, pathological fractures, spinal cord compression, and hypercalcemia—conditions that severely impact patients’ quality of life—but may also significantly increase the risk of death. Cancer bone metastasis is a complex, multistage cascade involving key steps such as the detachment of tumor cells from the primary tumor, their invasion and entry into the bloodstream, and their colonization, dormancy, and reactivation within the bone microenvironment. In recent years, exercise—as a safe and easily implementable non-pharmacological intervention—has demonstrated significant potential in inhibiting bone metastasis, improving the bone microenvironment, and alleviating related clinical symptoms. Its mechanisms of action may involve multiple levels. First, at the mechanical load level, mechanical signals generated by exercise can directly act on bone cells, inhibiting tumor cell infiltration and colonization by regulating endothelial cell permeability and osteoclast activity. Second, at the endocrine and paracrine levels, exercise can alter the expression profiles of microRNAs (such as miR-486 and miR-34b) carried by extracellular vesicles and particles (EVPs) in the circulation, thereby inhibiting tumor cell proliferation and migration by targeting cell cycle-related genes. Furthermore, exercise can remodel the immune microenvironment, enhance the cytotoxic activity of antitumor immune cells, and improve oxygen supply to tumor tissues, thereby alleviating hypoxia-induced immunosuppression. Finally, at the metabolic intervention level, exercise can induce systemic metabolic reprogramming, increasing the nutritional demands of normal tissues, thereby competing with tumor cells for nutrients and forming a “metabolic barrier” in distal organs such as bones. This article reviews the pathogenesis and classification of bone metastases, including osteolytic, osteogenic, and mixed types, and elaborates in detail on the complex processes by which tumor cells undergo colonization, dormancy, and reactivation within the bone microenvironment. Furthermore, based on a comprehensive analysis of human and animal studies on exercise interventions for bone metastases, this paper highlights the mechanisms by which exercise inhibits tumor formation, growth, and spread through the regulation of mechanical loading, extracellular vesicles and granules, as well as via immune and metabolic pathways, thereby interfering with bone metastasis. However, the array of exercise modalities and intensities available for patients grappling with the challenges of cancer-related bone metastases remains markedly constrained, and the formulation of tailored exercise prescriptions continues to be devoid of robust, evidence-based medical backing. This review aims to explore the effects of exercise on bone health in patients with bone metastases, analyze the factors and considerations that should be taken into account in exercise prescriptions, and provide new theoretical support and insights for developing personalized exercise programs for these patients and further advancing relevant clinical research. Future research directions should include the development of evidence-based, personalized exercise prescriptions and further clarification of the safety boundaries and best practice standards for exercise interventions, thereby promoting the advancement of relevant clinical research and ultimately improving patients’ clinical outcomes and quality of life.

Electroencephalography (EEG) is a non-invasive neurophysiological monitoring technique. It records the electrical activity of the cerebral cortex using electrodes placed on the scalp surface. Owing to its high safety, portability, and millisecond-level temporal resolution, EEG has been widely utilized in a variety of fields, including clinical diagnosis, brain-computer interfaces (BCIs), and cognitive neuroscience research. However, due to its microvolt-level amplitude, EEG is highly susceptible to various artifacts, including electrooculographic (EOG), electrocardiographic (ECG), electromyographic (EMG), and power line interference (PLI). These artifacts can obscure genuine neural activity and introduce spurious electrophysiological features. Consequently, they may compromise EEG signal quality, thereby reducing the reliability of downstream analyses. To address this issue, numerous EEG artifact removal methods have been developed, including both traditional denoising techniques and deep learning-based approaches. Traditional EEG denoising methods have long served as the primary solutions for artifact removal. Representative approaches include filtering, regression, and blind source separation. Although these methods have demonstrated effectiveness in specific scenarios, they suffer from several inherent limitations. Filtering assumes that artifacts and EEG signals can be separated in the frequency domain, but many artifacts, such as EOG and EMG, overlap with EEG spectra, which may lead to the loss of valuable neural information. Regression methods require high-quality artifact references to estimate and subtract contaminations, limiting their effectiveness in reference-free scenarios. Blind source separation can remove artifacts without external references, but it typically requires the number of EEG channels to exceed the number of sources, restricting its application in single- or low-channel EEG recordings. Deep learning-based EEG denoising methods address these limitations effectively. First, they learn the nonlinear mapping between contaminated and clean EEG directly from data in an end-to-end manner. This approach does not rely on assumptions about spectral separability, thereby preserving neural activity more completely. Second, the reference information is incorporated during the training phase, allowing the trained model to perform artifact removal independently without external references. Third, deep learning models can be flexibly designed to accommodate various recording setups, achieving robust denoising for both high-density and single-channel EEG. Collectively, these advantages enable deep learning-based methods to overcome the main challenges of traditional approaches, providing more accurate and reliable EEG signal recovery. The superior denoising performance of deep learning-based EEG denoising methods has attracted increasing attention in EEG artifact removal research. As a result, many deep learning-based denoising methods have been developed and successfully applied in neural engineering areas. However, a systematic review of the techniques and applications in this field is still lacking. To address this gap, this paper reviews recent advances in deep learning-based EEG denoising from four perspectives: technical principle, benchmark dataset, denoising model, and evaluation method. Representative applications in neural signal analysis and BCI decoding are also summarized. Furthermore, the advantage, existing challenge, and future research direction of deep learning-based EEG denoising are discussed. This review aims to provide valuable theoretical insights and technical guidance for researchers. It is also expected to promote further advances and broader applications of deep learning-based EEG denoising techniques.

ObjectiveUlcerative colitis is a prevalent immunoinflammatory disease. Th17/Treg cell imbalance and gut microbiota dysregulation are key factors in ulcerative colitis pathogenesis. The actin cytoskeleton contributes to regulating the proliferation, differentiation, and migration of Th17 and Treg cells. Wdr63, a gene containing the WD repeat domain, participates in the structure and functional modulation of actin cytoskeleton. Recent research indicates that WDR63 may serve as a regulator of cell migration and metastasis via actin polymerization inhibition. This article aims to explore the effect of Wdr63 deletion on Th17/Treg cells and ulcerative colitis. MethodsWe constructed Wdr63^-/- mice, induced colitis in mice using dextran sulfate sodium salt, collected colon tissue for histopathological staining, collected mesenteric lymph nodes for flow cytometry analysis, and collected healthy mouse feces for microbial diversity detection. ResultsCompared with wild-type colitis mice, Wdr63^-/- colitis mice had a more pronounced shortening of colonic tissue, higher scores on disease activity index and histological damage index, Treg cells decreased and Th17 cells increased in colonic tissue and mesenteric lymph nodes, a lower level of anti-inflammatory cytokine IL-10, and a higher level of pro-inflammatory cytokine IL-17A. In addition, WDR63 has shown positive effects on maintaining intestinal microbiota homeostasis. It maintains the balance of Bacteroidota and Firmicutes, promoting the formation of beneficial intestinal bacteria linked to immune inflammation. ConclusionWdr63 deletion aggravates ulcerative colitis in mice, WDR63 inhibits colonic inflammation likely by regulating Th17/Treg balance and maintains intestinal microbiota homeostasis.

The chemical constituents in dried roots of Atractylodes macrocephala were investigated in this study. Through utilizing normal-phase silica gel and ODS column chromatography, TLC, and semi-preparative HPLC, 4 new sesquiterpenoids were purified from the ethyl acetate extract of A. macrocephala. By various spectroscopic techniques, such as 1D and 2D NMR, HR-ESI-MS, IR, UV, and CD, their structures were identified as atractylmacron A (1), 9β-hydroxyasterolide (2), atractylenolide H (3), atractylenolide J (4). Compound 1 possesses a rare 6/7 bicyclic skeleton.

Eight compounds were isolated and purified from the ethyl acetate part of 70% acetone extract of Rehmannia glutinosa by various chromatographic techniques such as silica gel, MCI gel CHP-20, ODS, Toyopearl HW-40C, combined with TLC and semi-preparative HPLC. Their structures were elucidated by modern spectroscopy techniques (NMR, MS, UV, IR), and identified as neomartynoside A (1), osmanthuside B₆ (2), martynoside (3), isomartynoside (4), (E)-p-hydroxycinnamic acid (5), caffeic acid (6), ferulic acid (7), and methyl caffeate (8). Compound 1 is a new phenylethanol glycoside, which was identified as neomartynoside A. Compound 2 was isolated from Rehmannia glutinosa for the first time. In addition, compounds 2, 6 and 7 significantly increased relative glucose consumption, showing potential hypoglycemic activity.

ObjectiveNon-invasive magnetic stimulation technology has been widely used in the treatment of Alzheimer’s disease (AD), but there is a lack of convenient and timely methods for evaluating and providing feedback on the effectiveness of the stimulation, which can be used to guide the adjustment of the stimulation protocol. This study aims to explore the possibility of heart rate variability (HRV) in diagnosing AD and guiding AD magnetic stimulation intervention techniques. MethodsIn this study, we used a 40 Hz, 10 mT pulsed magnetic field to expose AD mouse models to whole-body exposure for 18 d, and detected the behavioral and electroencephalographic signals before and after exposure, as well as the instant electrocardiographic signals after exposure every day. ResultsUsing one-way ANOVA and Pearson correlation coefficient analysis, we found that some HRV indicators could identify AD mouse models as accurately as behavioral and electroencephalogram(EEG) changes (P<0.05) and significantly distinguish the severity of the disease (P<0.05), including rMSSD, pNN6, LF/HF, SD1/SD2, and entropy arrangement. These HRV indicators showed good correlation and statistical significance with behavioral and EEG changes (r>0.3, P<0.05); HRV indicators were significantly modulated by the magnetic field exposure before and after the exposure, both of which were observed in the continuous changes of electrocardiogram (ECG) (P<0.05), and the trend of the stimulation effect was more accurately observed in the continuous changes of ECG. ConclusionHRV can accurately reflect the pathophysiological changes and disease degree, quickly evaluate the effect of magnetic stimulation, and has the potential to guide the pattern of magnetic exposure, providing a new idea for the study of personalized electromagnetic neuroregulation technology for brain diseases.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

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.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.