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 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.

ObjectiveTobacco-related diseases remain one of the leading preventable public health challenges worldwide and are among the primary causes of premature death. In recent years, accumulating evidence has supported the classification of nicotine addiction as a chronic brain disease, profoundly affecting both brain structure and function. Despite the urgency, effective diagnostic methods for smoking addiction remain lacking, posing significant challenges for early intervention and treatment. To address this issue and gain deeper insights into the neural mechanisms underlying nicotine dependence, this study proposes a novel graph neural network framework, termed TI-GNN. This model leverages functional magnetic resonance imaging (fMRI) data to identify complex and subtle abnormalities in brain connectivity patterns associated with smoking addiction. MethodsThe study utilizes fMRI data to construct functional connectivity matrices that represent interaction patterns among brain regions. These matrices are interpreted as graphs, where brain regions are nodes and the strength of functional connectivity between them serves as edges. The proposed TI-GNN model integrates a Transformer module to effectively capture global interactions across the entire brain network, enabling a comprehensive understanding of high-level connectivity patterns. Additionally, a spatial attention mechanism is employed to selectively focus on informative inter-regional connections while filtering out irrelevant or noisy features. This design enhances the model’s ability to learn meaningful neural representations crucial for classification tasks. A key innovation of TI-GNN lies in its built-in causal interpretation module, which aims to infer directional and potentially causal relationships among brain regions. This not only improves predictive performance but also enhances model interpretability—an essential attribute for clinical applications. The identification of causal links provides valuable insights into the neuropathological basis of addiction and contributes to the development of biologically plausible and trustworthy diagnostic tools. ResultsExperimental results demonstrate that the TI-GNN model achieves superior classification performance on the smoking addiction dataset, outperforming several state-of-the-art baseline models. Specifically, TI-GNN attains an accuracy of 0.91, an F1-score of 0.91, and a Matthews correlation coefficient (MCC) of 0.83, indicating strong robustness and reliability. Beyond performance metrics, TI-GNN identifies critical abnormal connectivity patterns in several brain regions implicated in addiction. Notably, it highlights dysregulations in the amygdala and the anterior cingulate cortex, consistent with prior clinical and neuroimaging findings. These regions are well known for their roles in emotional regulation, reward processing, and impulse control—functions that are frequently disrupted in nicotine dependence. ConclusionThe TI-GNN framework offers a powerful and interpretable tool for the objective diagnosis of smoking addiction. By integrating advanced graph learning techniques with causal inference capabilities, the model not only achieves high diagnostic accuracy but also elucidates the neurobiological underpinnings of addiction. The identification of specific abnormal brain networks and their causal interactions deepens our understanding of addiction pathophysiology and lays the groundwork for developing targeted intervention strategies and personalized treatment approaches in the future.

Quercetin can mediate the activation of mitogen-activated protein kinase(MAPK)signaling pathways to prevent osteoporosis(OP).This paper comprehensively discusses the interrelationship between MAPK and osteoporosis-related cells based on the latest domestic and international research.Additionally,it elucidates the research progress of quercetin in mediating the MAPK signaling pathway for OP prevention.The aim is to provide an effective foundation for the clinical prevention and treatment of OP and the in-depth development of quercetin.

Humans ; Vascular Stiffness ; Coal Mining ; Occupational Diseases/epidemiology* ; Risk Factors ; Coal ; Miners

In this paper, the antitussive and expectorant activity of platycodin D (PD) were studied by constructing a mouse cough induced by concentrated ammonia water and a mouse trachea phenol red excretion model. The mechanism of antitussive and expectorant effect of PD was studied by metabolomics. The animal experiment was approved by the Animal Ethics Committee of Jiangxi University of Chinese Medicine (approval number: JZLLSC-20220739). Then mice were randomly divided into the normal, model, positive drug, PD low-dose, PD medium-dose and PD high-dose group. The antitussive and expectorant effects of PD were evaluated using a cough mouse model induced by concentrated ammonia water and a mouse tracheal phenol red excretion model, respectively. UHPLC-LTQ-Orbitrap-MS was used to identify the metabolites of mouse lung tissue, and multivariate statistical analysis method of orthogonal partial least squares discriminant analysis (OPLS-DA) was used for metabolites profile analysis. The differential metabolites were screened by variable projected importance value (VIP) and t-test results. Pathways for enrichment of differentiated metabolites were analyzed using the MetaboAnalyst platform. The comparative method was applied to analyze the differences in mechanisms of PD, Deapio-platycodin D (DPD) and total platycosides fraction. The results showed that PD at different concentrations could significantly prolong (P < 0.05) the incubation period of cough mice induced by ammonia water, reduce the coughs frequency, and significantly increase (P < 0.05) the amount of phenol red excretion in phenol red excretion model mice. PD could regulate 6 metabolic pathways of phenylalanine, tyrosine and tryptophan biosynthesis, linoleic acid metabolism, phenylalanine metabolism, glycerophospholipid metabolism, and tyrosine metabolism to exert antitussive effect. It could also regulate 8 metabolic pathways of linoleic acid metabolism, glyoxylic acid and dicarboxylic acid metabolism, glycerol phospholipid metabolism, citric acid cycle and arachidonic acid metabolism to exert an expectorant effect. However, only linoleic acid metabolism and glycerophospholipid metabolism could be regulated by the PD, total platycosides fraction and DPD, which may be ascribed to the structural difference of the platycosides and the interaction between platycosides and the intestinal microbiota. Functional analysis showed that these metabolic pathways are closely related to the regulatory mechanisms of anti-inflammatory response, immune function regulation, neurotransmitter release, cell signal transduction, energy metabolism and cell apoptosis. This study shows that PD possesses good antitussive and expectorant activities. In addition, the mechanism difference of PD, total platycosides fraction and DPD imply that the apiose in PD and the interaction between PD and intestinal microbiota could exert an important effect on the antitussive and expectorant mechanism of the platycosides.

It has become an industry consensus that self-assembled nanoparticles (SAN) are formed by molecular recognition of chemical components in traditional Chinese medicine during the decoction process. The insoluble components in the decoction are mostly in the form of nanoparticles, which can improve the problem of poor water solubility. However, the transfer rate of these insoluble components in the decoction is still very low, which limits the efficacy of the drug. This study aimed to refine the traditional decoction self-assembly phenomenon. The self-assembled nanoparticles were constructed by micro-precipitation method (MP-SAN), and characterized by particle size, zeta potential, stability index and morphology. The formation of MP-SAN and alterations in related physicochemical properties were evaluated using modern spectroscopic and thermal analysis techniques. The quality value transmitting pattern of lignan components within the MP-SAN was assessed via high performance liquid chromatography (HPLC). The MP-SAN showed sphere-like structure with uniform morphology, particle size of (245.3 ± 3.2) nm, polydispersity index (PDI) of (0.13 ± 0.03), zeta potential of (-48.9 ± 5.9) mV and stability index (SI) of (86.05% ± 2.27%). Comprehensive analyses using ultraviolet visible spectroscopy, Fourier transform infrared spectroscopy, differential scanning calorimetry, and other techniques confirmed molecular recognition between the decoction and ethanol extraction, leading to electron rearrangement under the influence of non-covalent bonding. This resulted in the formation of nanoparticles possessing superior thermal stability. As determined by HPLC, the encapsulation rates of the index components in the MP-SAN were all greater than 75% (dehydrodiconiferyl alcohol: 77.00%; herpetolide A: 78.57%; herpetrione: 94.53%), and the transfer rates were all higher than 65% (dehydrodiconiferyl alcohol: 96.01%; herpetolide A: 67.86%; herpetrione: 65.55%), which were 1.34, 1.38 and 4.81 times compared with those of the traditional decoction. In summary, this study successfully constructed the MP-SAN based on micro-precipitation method to achieve high transfer rate and high encapsulation rate of insoluble components in docoction, which provides a pharmaceutics idea for the efficient utilization of pharmacodynamic substance basis of traditional Chinese medicine.