Objective: To prepare the erythrocyte-liposome drug delivery system to enhance the therapeutic effect of drugs on tumors and inhibit tumor metastasis. Methods: This study prepared and characterized paclitaxel (PTX)-plerixafor (AMD3100) liposomes (Lips), developed the erythrocyte-liposome drug delivery system, and evaluated its targeting efficiency and therapeutic efficacy through a series of in vitro cellular and in vivo animal experiments. Results: The particle size of PTX-AMD-Lips was (186.4±0.83) nm. Drug encapsulation efficiency of PTX-AMD-Lips was (75.50±5.27)% for PTX and (88.31±2.45)% for AMD. The Binding efficiency between RBC and liposomes in the drug delivery system was (69.93±2.55)%. Vitro cellular experiments revealed that PTX-AMD-Lips significantly inhibited tumor cell migration. In vivo animal experiments, the erythrocyte-liposome drug delivery system significantly increased drug accumulation in the lungs. At the experimental endpoint, the quantitative fluorescence signal of tumor size measured (4.04±0.44)×10 for the PTX-Lips group, and (5.14±3.40)×10 for the RBC-PTX-AMD-Lips group. Conclusion: The erythrocyte-liposome drug delivery system could enhance the lung-specific targeting capability of liposomes, kill tumor cells and suppress further metastasis effectively.

To improve the targeted therapeutic effect of magnolol (Mag) on neuroblastoma, Mag-loaded mitochondria-targeting immunoliposomes modified by datuximab (aGD2) and triphenylphosphine (TPP) (Mag/aGD2-T-ILN) were prepared, and their physicochemical properties, targeting characteristics and anti-tumor activity were evaluated. Physico-chemical properties showed that the surface of Mag/aGD2-T-ILN was smooth and spherical, with good dispersibility. The particle sizes, PDI and Zeta potentials of Mag/aGD2-T-ILN were measured to be (136.5 ± 5.1) nm, 0.184 ± 0.010 and (27.5 ± 3.6) mV, respectively. Mag/aGD2-T-ILN could release the drug continuously and slowly, and maintain good stability at 4 ℃. Cytotoxicity test exhibited that the IC50 of 2-ME/aGD2-T-ILN was (4.07 ± 0.48) µmol/L, and compared with free Mag, the toxicity of Mag/aGD2-T-ILN to SH-SY5Y cells increased by 6.4 times. Cellular binding and uptake assays suggested that Rho-aGD2-T-ILN could specifically target GD2-positive tumor cells and then further reach their mitochondria. Therapeutic efficacy indicated that Mag/aGD2-T-ILN could better suppress the growth of SH-SY5Y tumor cells in the body with lower toxicity and less side-effects. The results demonstrated that the Mag/aGD2-T-ILN nanoparticles system could achieve intracellular endocytosis through specific binding of antibodies and antigens between the carrier and the surface of tumor cells and electrostatic interaction, then effectively delivered and released the drugs into mitochondria by crossing the mitochondrial phospholipid membrane through TPP, and thus achieving mitochondria-targeting therapy of Mag/aGD2-T-ILN. Through the construction of this active targeting delivery system, the clinical application value of datuximab and Mag is improved, providing a novel approach for the clinical treatment of neuroblastoma.

Objective: To investigate the regulatory role of miR-6824-3p in macrophage function and its molecular mechanism in inhibiting hepatitis B virus (HBV) replication, thereby providing experimental evidence to elucidate the immune regulatory mechanisms underlying persistent HBV infection. Methods: miR-6824-3p mimic and inhibitor were transfected into human THP-1-induced macrophages. Real-time quantitative PCR (qRT-PCR), enzyme-linked immunosorbent assay (ELISA), neutral red uptake, reactive oxygen species (ROS) production, and fluorescent latex particle phagocytosis assays were employed to evaluate the effects of miR-6824-3p on macrophage phenotype and function. Through a combination of bioinformatics analysis, dual luciferase reporter assays, western blot, and siRNA interference techniques, we identified the target gene of miR-6824-3p and examined their effects on downstream signaling pathways. qRT-PCR and western blot analyses were performed to assess the impact of miR-6824-3p-regulated macrophages on HBV DNA, pgRNA, cccDNA, and HBV-associated antigen levels in HepAD38 cells. Key effector molecules were identified through neutralization assays. Results: miR-6824-3p mimic significantly promoted the expression and secretion of proinflammatory factors, such as TNF-α and IL-1β, in macrophages (P<0.001), while concurrently reducing ROS production and phagocytosis (P<0.05). Furthermore, miR-6824-3p downregulated NRAS expression in macrophages, which was accompanied by a reduction in MAPK signalling path-way activity (p-MEK, p-ERK). Compared to the control group, the medium of macrophages with overexpressed miR-6824-3p inhibited the expression of HBV DNA, pgRNA, cccDNA, and HBV-associated antigens HBsAg, HBeAg, and HBcAg in HepAD38 cells (P<0.01). Similar results were also observed in the co-culture system of macrophages with HepAD38 cells. The addition of TNF-α neutralizing antibodies markedly attenuated the aforementioned antiviral effects (P<0.001). Conclusion: miR-6824-3p targets NRAS to affect the downstream MAPK signaling pathway, regulating the immune function of macrophages. The TNF-α induced by miR-6824-3p is one of the key molecules that suppress HBV replication. This study provides evidence for further elucidating the molecular mechanisms by which miRNAs influence HBV replication via modulating the host immune microenvironment.

ObjectiveTo construct a risk prediction model for carotid artery plaques in high-risk populations of stroke based on five machine learning methods. MethodsThe clinical information of the high-risk population of stroke was collected. Factor analysis and statistical analysis of syndrome elements were conducted on their traditional Chinese medicine （TCM） symptoms and tongue and pulse manifestations. On the basis of the results of factor analysis and variable screening， five machine learning methods-classification and regression tree （CART） decision tree， support vector machine （SVM）， back propagation（BP） neural network， logistic regression， and random forest-were used to construct the risk prediction model for carotid artery plaques. ResultsThe most common TCM syndrome elements in the high-risk population of stroke was Qi deficiency. The scores of Qi deficiency， Yin deficiency， and Yang deficiency in the population with carotid artery plaques were higher than those without carotid artery plaques （P<0.05）. The CART decision tree， SVM， logistic regression， BP neural network， and random forest models showed the areas under the receiver operating characteristic （ROC） curves of 0.71， 0.75， 0.76， 0.76， and 0.75， the accuracy rates of 68.94%， 69.27%， 69.44%， 70.10%， and 69.60%， the precision rates of 68.56%， 68.53%， 68.75%， 69.74%， and 69.42%， the recall rates of 68.91%， 67.93%， 67.92%， 68.10%， and 67.31%， and the F₁ values of 0.69， 0.68， 0.68， 0.68， and 0.68， respectively. ConclusionAmong the high-risk population of stroke， the most frequently distributed TCM syndrome element is Qi deficiency， with the rest mainly being fire heat， Yin deficiency， Yang deficiency， phlegm dampness， blood stasis， and Qi stagnation. Deficiency syndrome may be a major factor leading to carotid artery plaques in the high-risk population of stroke. The BP neural network model demonstrates better performance in predicting the risk of carotid artery plaques in the high-risk population of stroke. People with carotid artery plaques are more likely to present with symptoms such as a heavy head， dizziness， headache， and thready pulse. The primary community benefits more widely when the BP neural network model is adopted to predict the risk of carotid artery plaques in the high-risk population of stroke over 40 years old.

ObjectiveTo construct a risk prediction model for carotid artery plaques in high-risk populations of stroke based on five machine learning methods. MethodsThe clinical information of the high-risk population of stroke was collected. Factor analysis and statistical analysis of syndrome elements were conducted on their traditional Chinese medicine （TCM） symptoms and tongue and pulse manifestations. On the basis of the results of factor analysis and variable screening， five machine learning methods-classification and regression tree （CART） decision tree， support vector machine （SVM）， back propagation（BP） neural network， logistic regression， and random forest-were used to construct the risk prediction model for carotid artery plaques. ResultsThe most common TCM syndrome elements in the high-risk population of stroke was Qi deficiency. The scores of Qi deficiency， Yin deficiency， and Yang deficiency in the population with carotid artery plaques were higher than those without carotid artery plaques （P<0.05）. The CART decision tree， SVM， logistic regression， BP neural network， and random forest models showed the areas under the receiver operating characteristic （ROC） curves of 0.71， 0.75， 0.76， 0.76， and 0.75， the accuracy rates of 68.94%， 69.27%， 69.44%， 70.10%， and 69.60%， the precision rates of 68.56%， 68.53%， 68.75%， 69.74%， and 69.42%， the recall rates of 68.91%， 67.93%， 67.92%， 68.10%， and 67.31%， and the F₁ values of 0.69， 0.68， 0.68， 0.68， and 0.68， respectively. ConclusionAmong the high-risk population of stroke， the most frequently distributed TCM syndrome element is Qi deficiency， with the rest mainly being fire heat， Yin deficiency， Yang deficiency， phlegm dampness， blood stasis， and Qi stagnation. Deficiency syndrome may be a major factor leading to carotid artery plaques in the high-risk population of stroke. The BP neural network model demonstrates better performance in predicting the risk of carotid artery plaques in the high-risk population of stroke. People with carotid artery plaques are more likely to present with symptoms such as a heavy head， dizziness， headache， and thready pulse. The primary community benefits more widely when the BP neural network model is adopted to predict the risk of carotid artery plaques in the high-risk population of stroke over 40 years old.

Purpose: Gold-standard urodynamic examination is widely used in the diagnosis and treatment of lower urinary tract dysfunction. The purpose of urodynamic quality control is to standardize urodynamic examination and ensure its clinical reference value. In our study, we attempted to use a deep learning (DL) algorithm model, mainly for the recognition of typical urodynamic signal, to help physicians complete high-quality urodynamic examinations. Methods: Urodynamic image data from 2 cohorts of adult patients with neurogenic bladder were used: (1) 300 patients with neurogenic bladder in our center from 2012 to 2018 (1,960 images used to train and validate the DL model); and (2) 100 patients with neurogenic bladder from 2020 to 2021 (695 images used to test the performance of the DL model). This resulted in a total of 2,655 images to train, validate and test the DL algorithm to predict the urdynamic signals. Results: Yolov5l had the best detection performance and the highest comprehensive index score (F1, 0.81; mean average precision, 0.83). Our study is a retrospective single-center study, and the generalization ability of the model has not been verified. Conclusions DL algorithms can help operators identify typical urodynamic signals in real time, improve the interpretation and quality of urodynamic examination, and benefit patients.

Purpose: Gold-standard urodynamic examination is widely used in the diagnosis and treatment of lower urinary tract dysfunction. The purpose of urodynamic quality control is to standardize urodynamic examination and ensure its clinical reference value. In our study, we attempted to use a deep learning (DL) algorithm model, mainly for the recognition of typical urodynamic signal, to help physicians complete high-quality urodynamic examinations. Methods: Urodynamic image data from 2 cohorts of adult patients with neurogenic bladder were used: (1) 300 patients with neurogenic bladder in our center from 2012 to 2018 (1,960 images used to train and validate the DL model); and (2) 100 patients with neurogenic bladder from 2020 to 2021 (695 images used to test the performance of the DL model). This resulted in a total of 2,655 images to train, validate and test the DL algorithm to predict the urdynamic signals. Results: Yolov5l had the best detection performance and the highest comprehensive index score (F1, 0.81; mean average precision, 0.83). Our study is a retrospective single-center study, and the generalization ability of the model has not been verified. Conclusions DL algorithms can help operators identify typical urodynamic signals in real time, improve the interpretation and quality of urodynamic examination, and benefit patients.