Isoliquiritigenin (ISL) is a chalcone compound isolated from licorice, known for its anti-diabetic, anti-cancer, and antioxidant properties. Our previous study has demonstrated that ISL effectively lowers blood glucose levels in type 2 diabetes mellitus (T2DM) mice and improves disturbances in glucolipid and energy metabolism induced by T2DM. This study aims to further investigate the effects of ISL on alleviating abnormal endoplasmic reticulum stress (ERS) caused by T2DM and to elucidate its molecular mechanisms. In vivo experiments were conducted using 8-week-old SPF male C57BL/6J mice. The T2DM animal model was established by high-fat and high-sugar diet combined with intraperitoneal injections of streptozotocin (STZ), in compliance with the ethical guidelines set by the Animal Welfare Committee of Beijing University of Chinese Medicine (approval number: BUCM-2022021503-1134). In vitro experiments employed human liver cancer HepG2 cells, which were induced with tunicamycin (TM) to establish the ERS cell model. Transcriptomic sequencing was used to analyze changes in gene expression in the liver samples of T2DM mice following ISL treatment. Real-time quantitative polymerase chain reaction (RT-qPCR) was employed to assess the regulatory effects of ISL on key ERS genes. Enzyme-linked immunosorbent assay (ELISA), Western blot (WB), and immunofluorescence techniques were used to evaluate ISL's effects on ERS-related proteins. Results indicate that ISL significantly downregulates the expression of ERS-related genes, reduces the level of glucose-regulated protein 78 (GRP78), and inhibits the phosphorylation of protein kinase RNA-like endoplasmic reticulum kinase (PERK), thereby alleviating abnormal ERS induced by T2DM. Additionally, ISL increases the protein levels of insulin receptor substrate (IRS) 1 and IRS2 and enhances the phosphorylation of protein kinase B (Akt), thereby improving insulin sensitivity. In conclusion, ISL is able to alleviate T2DM associated symptoms by improving abnormal ERS and enhancing insulin sensitivity.

OBJECTIVE To investigate the clinical efficacy of integrating pharmacists into family health teams （FHTs） for long-term medication therapeutical management （MTM） in stroke patients， and empirically evaluate the service model. METHODS A pharmacist team， jointly established by clinical and community pharmacists from the Affiliated Suzhou Hospital of Nanjing Medical University （hereinafter referred to as “our hospital”）， developed a pharmacist-supported MTM model integrated into FHTs. Using a prospective randomized controlled design， 170 stroke patients discharged from our hospital （July 2022-December 2023） and enrolled in FHTs at Suzhou Runda Community Hospital were randomly divided into trial group （88 cases） and control group （82 cases） according to random number table. The control group received routine FHTs care （without pharmacist involvement in the team collaboration）， while the trial group xhz8405@126.com received 12-month MTM services supported by pharmacists via an information platform. These services specifically included innovative interventions such as personalized medication regimen optimization based on the MTM framework， dynamic medication adherence management， medication safety monitoring， a home medication assessment system， and distinctive service offerings. Outcomes of the 2 grousp were compared before and after intervention， involving medication adherence （adherence rate， adherence score）， compliance rates for stroke recurrence risk factors [blood pressure， low-density lipoprotein cholesterol （LDL-C）]， and incidence of adverse drug reactions （ADR）. RESULTS After 12 months， the trial group exhibited significantly higher medication adherence rates， improved adherence scores， higher compliance rates for blood pressure and LDL-C targets compared to the control group （P＜0.05）. The incidence of ADR in the trial group （4.55%） was significantly lower than that in the control group （8.11%）， though the difference was not statistically significant （P＞ 0.05）. CONCLUSIONS Pharmacist involvement in FHTs to deliver MTM services significantly enhances medication adherence and optimizes risk factor for stroke recurrence， offering practical evidence for advancing pharmaceutical care in chronic disease management under the family doctor system.

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.

Objective:To investigate the value of a novel technique called visual transient elastography (ViTE) and liver steatosis analysis (LiSA) in assessing liver function injury in hepatitis B virus-related hepatocellular carcinoma (HBV-related HCC) patients after ablation therapy.Methods:A total of 129 HBV-related HCC patients in Harbin Medical University Cancer Hospital from January 2022 to August 2023 were retrospectively analyzed.ViTE and LiSA examinations were applied to record the liver stiffness E value and LiSA value before ablation. An albumin-bilirubin (ALBI) score was constructed using laboratory indicators. Spearman correlation analysis was used to assess the correlation between E value, LiSA value and ALBI score, body mass index (BMI). According to the change of ALBI grade in perioperative period, the patients could be divided into two groups: liver function unchanged group and liver function injury group. Univariate and multivariate statistical methods were used to analyze related factors affecting changes in liver function after ablation, followed by establishing a predictive model.Results:Spearman analysis showed a strong positive correlation between E value and ALBI score ( rs=0.686, P<0.001), and LiSA value was weakly positively correlated with BMI ( rs=0.338, P<0.001). There were no significant correlations between E value and BMI, LiSA value and ALBI score (all P>0.05). Univariate analysis showed that differences of age, BMI, ablation parameters, E value, and LiSA value in the two groups were statistically significant (all P<0.05). Multivariate analysis showed that E value, LiSA value, and ablation time were independent predictors of hepatic dysfunction after ablation (all P<0.05). AUC was 0.892 indicating high accuracy in the predictive model based on above indicators. Conclusions:Under the real-time ultrasound guidance, a non-invasive liver function injury prediction model based on ViTE and LiSA technology can offer personalized predictions for ablative perioperative changes in liver function among HBV-related HCC patients.

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.