Oridonin, a well-known traditional Chinese herbal medicinal product isolated from Isodon rubescens (Hemsl.) H.Hara, has many potential properties, including anti-inflammatory and antioxidant activities. However, there is no evidence whether oridonin have a protective effect on atherosclerosis. This study focused on the effects of oridonin on oxidative stress and inflammation generated from atherosclerosis. The therapeutic effect on atherosclerosis was evaluated by intraperitoneal injection of oridonin in a high-fat fed ApoE^−/− mouse model. We isolated mouse peritoneal macrophages and detected the effect of oridonin on oxidized low-density lipoprotein-induced lipid deposition. Oil red O staining, Masson's staining, dihydroethidium fluorescence staining, immunohistochemical staining, western blotting analysis, immunofluorescence, enzyme-linked immunosorbent assay and quantitative real-time PCR were used to evaluate the effect on atherosclerosis and explore the mechanisms. Oridonin treatment significantly alleviated the progression of atherosclerosis, reduced macrophage infiltration and stabilized plaques. Oridonin could significantly inhibit inflammation associated with NLRP3 activation. Oridonin significantly reduced oxidative stress by blocking Nrf2 ubiquitination and degradation. We also found that oridonin could prevent the formation of foam cells by increasing lipid efflux protein and reducing lipid uptake protein in macrophages. Oridonin has a protective effect on atherosclerosis in ApoE^−/− mice, which may be related to the inhibition of NLRP3 and the stabilization of Nrf2. Therefore, oridonin may be a potential therapeutic agent for atherosclerosis.

Hepatocellular carcinoma (HCC) is one of the most frequent malignancies in the world. Although increasing evidence supports the role of heterogeneous ribonucleoprotein particle A1 (HNRNP A1) in tumor progression, the function of HNRNP A1 in HCC remains unclear. Here, we focused on the role of HNRNP A1 in the development of HCC. In this study, we found HNRNP A1 participates in many aspects of HCC, such as progression and prognosis. Our results showed that HNRNP A1 is upregulated in human HCC tissues and cell lines. High expression of HNRNP A1 can promote the proliferation, migration, and invasion in HCC cells and accelerate tumor progression in mice. Moreover, we found that HNRNP A1 prevents the senescence process of HCC cells. Knocking down of HNRNP A1 promotes the expression of P16^INK4, which arrests the cell cycle and then induces the senescence phenotype in HCC cells. Furthermore, we found that HNRNP A1 regulated necroptosis and mitochondrial dynamics. In summary, our study indicates that HNRNP A1 promotes the development of HCC, which suggests a potential therapeutic target for HCC.

Cy₁₃₉₅-NPs selectively accumulate in gastric cancer cells through attachment mediated by integrin α_vβ₃ and target the mitochondria, achieving NIR fluorescence imaging and PDT via the generation of ¹O₂ under laser irradiation.

Background: Patients with carotid atherosclerotic stenosis (CAS) often have varying degrees of cognitive decline. However, there is little evidence regarding how brain morphological and functional abnormalities impact the cognitive decline in CAS patients. This study aimed to determine how the brain morphological and functional changes affected the cognitive decline in patients with CAS.Methods: The brain morphological differences were analyzed using surface and voxel-based morphometry, and the seed-based whole-brain functional connectivity (FC) abnormalities were analyzed using resting-state functional magnetic resonance imaging. Further, mediation analyses were performed to determine whether and how morphological and FC changes affect cognition in CAS patients.Results: The CAS-MCI (CAS patients with mild cognitive impairment) group performed worse in working memory, verbal fluency, and executive time. Cortical thickness (CT) of the left postcentral and superiorparietal were significantly reduced in CAS-MCI patients. The gray matter volume (GMV) of the right olfactory, left temporal pole (superior temporal gyrus) (TPOsup.L), left middle temporal gyrus (MTG.L), and left insula (INS.L) were decreased in the CAS-MCI group. Besides, decreased seed-based FC between TPOsup.L and left precuneus, between MTG.L and TPOsup.L, and between INS.L and MTG.L, left middle frontal gyrus, as well as Superior frontal gyrus, were found in CAS-MCI patients. Mediation analyses demonstrated that morphological and functional abnormalities fully mediated the association between the maximum degree of carotid stenosis and cognitive function.Conclusion: Multiple brain regions have decreased GMV and CT in CAS-MCI patients, along with disrupted seed-based FC. These morphological and functional changes play a crucial role in the cognitive impairment in CAS patients.

Objective: To develop and validate a new prediction model based on the Lass-logistic regression with inflammatory serologic markers for the assessment of carotid plaque stability, providing clinicians with a reliable tool for risk stratification and decision-making in the management of carotid artery disease.Methods: In this study, we retrospectively collected the data of the patients who underwent carotid endarterectomy (CEA) from 2019 to 2023 in Nanjing Drum Tower Hospital. Demographic characteristics, vascular risk factors, and the results of preoperative serum biochemistry were measured and collected. The risk factors for vulnerable carotid plaque were analyzed. A Lasso-logistic regression prediction model was developed and compared with traditional logistic regression models. The Akaike information criterion (AIC) and Bayesian information criterion (BIC) were used to evaluate the performance of three models.Results: A total of 131 patients were collected in this study, including 66 (50.4%) in the vulnerable plaque group and 65 (49.6%) in the stable plaque group. The final Lasso-logistic regression model included 4 features:IL-6, TSH, TSHI, and TT4RI; AIC = 161.6376, BIC = 176.0136, both lower than the all-variable logistic regression model (AIC = 181.0881, BIC = 261.5936), and the BIC was smaller than the stepwise logistic regression model (AIC = 154.024, BIC = 179.9007). Finally, the prediction model was constructed based on the variables screened by the Lasso regression, and the model had favorable discrimination and calibration.Conclusions: The noninvasive prediction model based on IL-6 and TSHI is a quantitative tool for predicting vulnerable carotid plaques. It has high diagnostic efficacy and is worth popularizing and applying.

This study aims to develop and validate a deep learning model based on Cone Beam Computed Tomography (CBCT) radiomic features to achieve early detection of potential carotid atherosclerosis in periodontitis patients. The study utilised data from 279 observations, each with 206 features, to distinguish between periodontitis patients with and without concomitant carotid atherosclerosis. To address class imbalance, Synthetic Minority Over-sampling Technique(SMOTE) oversampling was applied (dup_size = 1), increasing the sample size to 390 observations. A bootstrap method (n_bootstrap = 1000) was employed for feature selection. In each iteration, a dataset was created by resampling with replacement. Features were first filtered using Spearman’s rank correlation to remove redundant variables (correlation coefficient > 0.8), followed by Lasso regression with ten-fold cross-validation to select predictive variables based on non-zero coefficients. High-frequency features identified through 1000 iterations underwent a second round of bootstrap analysis, where Logistic Regression combined with the Akaike Information Criterion (AIC) was used to determine the final variable set. This rigorous process ensured optimal feature selection for developing an effective early detection model for carotid atherosclerosis in periodontitis patients. The study analyzed data from 279 observations, with each observation characterized by 206 features, to differentiate between periodontitis patients with concurrent carotid atherosclerosis and those without. After SMOTE oversampling, the dataset was increased to 390 observations. As stated in the Methods, SMOTE was applied after baseline analysis to augment the dataset for model development. Feature selection through bootstrap methods identified 26 high-frequency features (> 500 times), which were further refined to a final set of 20 features using Logistic Regression combined with AIC. Three machine learning models—Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF)—were developed and evaluated using five-fold cross-validation. The best-performing model was the RF model, achieving an Area Under the Curve(AUC) of 0.892, sensitivity of 0.957, specificity of 0.710, and accuracy of 0.859. Receiver Operating Characteristic(ROC) curves and calibration plots demonstrated good predictive performance and model calibration across all three models. Decision curve analysis showed that the RF model provided the highest net benefit across a range of risk thresholds, indicating its potential for clinical utility in early detection of carotid atherosclerosis in periodontitis patients. This study developed a random forest model using CBCT radiomics to detect carotid atherosclerosis in periodontitis patients early. After rigorous feature selection and five-fold cross-validation, it achieved an AUC of 0.892, with sensitivity of 0.957 and specificity of 0.710. The model shows high predictive performance and clinical utility, offering an effective tool for early detection.