ObjectiveTo investigate the effect of laminin subunit α3 （LAMA3） on the epithelial-mesenchymal transition （EMT）， invasion， and metastasis abilities of pancreatic cancer （PC）. MethodsA comprehensive analysis was performed for tumor- and EMT-related databases to identify the EMT genes associated with PC， especially LAMA3. The methods of qRT-PCR and Western blot were used to measure the expression level of LAMA3 in PC tissue and cell lines； immunofluorescence assay was used to determine the localization of LAMA3 in PANC-1 cells； Transwell assay was used to investigate the effect of LAMA3 on the invasion and migration abilities of PC cells. The t-test was used for comparison of continuous data between groups. ResultsThe analysis of the TCGA database identified 3 EMT-related oncogenes for PC， i.e.， LAMA3， AREG， and SDC1. The LASSO-Cox regression model showed that LAMA3 had the most significant impact on the prognosis of PC （risk score=0.256 1×LAMA3+0.043 1×SDC1+0.071 4×AREG）. The Cox model and nomogram showed that the high expression of LAMA3 was an independent risk factor for the poor prognosis of PC （hazard ratio=1.32， 95% confidence interval： 1.07‍ ‍—‍ 1.62， P<0.01）. Experimental results showed that there was a significant increase in the expression of LAMA3 in pancreatic cancer tissue compared with the normal pancreatic tissue. Compared with the HPDE cell line， there were varying degrees of increase in the expression of LAMA3 in pancreatic cancer AsPC-1， BxPC-3， PANC-1， MIA PaCa-2， and SW1990 cell lines， with the highest expression level in PANC-1 cells. The enrichment analysis showed that LAMA3 was associated with the biological processes and signaling pathways such as EMT， collagen metabolism， extracellular matrix degradation， the TGF-β pathway， and the PI3K pathway. After the knockdown of LAMA3， there were significant reductions in the expression levels of N-Cadherin， Vimentin， and Snail， while there was a significant increase in the expression level of E-Cadherin. Transwell assay showed that there were significant reductions in the invasion and migration abilities of PANC-1 cells after the knockdown of LAMA3. ConclusionLAMA3 is highly expressed in PC and can promote the EMT， invasion， and migration of PC cells， and therefore， LAMA3 may be used as a novel diagnostic marker and a new therapeutic target for PC.

BACKGROUND: Salvianolate is a compound mainly composed of salvia magnesium acetate, which is extracted from the Chinese herb Salvia miltiorrhiza . In recent years, salvianolate injection has been widely used in the treatment of cardiovascular diseases, but the mechanism of how it can alleviate cardiotoxicity remains unclear. METHODS: The cardiac injury model was constructed by treatment with doxorubicin (Dox) or azithromycin (Azi) in zebrafish larvae. Heart phenotype, heart rate, and cardiomyocyte apoptosis were observed in the study. RNA-sequencing (RNA-seq) analysis was used to explore the underlying mechanism of salvianolate treatment. Moreover, cardiomyocyte autophagy was assessed by in situ imaging. In addition, the miR-30a/becn1 axis regulation by salvianolate was further investigated. RESULTS: Salvianolate treatment reduced the proportion of pericardial edema, recovered heart rate, and inhibited cardiomyocyte apoptosis in Dox/Azi-administered zebrafish larvae. Mechanistically, salvianolate regulated the lysosomal pathway and promoted autophagic flux in zebrafish cardiomyocytes. The expression level of becn1 was increased in Dox-induced myocardial tissue injury after salvianolate administration; overexpression of becn1 in cardiomyocytes alleviated the Dox/Azi-induced cardiac injury and promoted autophagic flux in cardiomyocytes, while becn1 knockdown blocked the effects of salvianolate. In addition, miR-30a, negatively regulated by salvianolate, partially inhibited the cardiac amelioration of salvianolate by targeting becn1  directly. CONCLUSION This study has proved that salvianolate reduces cardiomyopathy by regulating autophagic flux through the miR-30a/becn1 axis in zebrafish and is a potential drug for adjunctive Dox/Azi therapy.
Animals ; Zebrafish ; MicroRNAs/genetics* ; Autophagy/drug effects* ; Myocytes, Cardiac/metabolism* ; Cardiomyopathies/metabolism* ; Beclin-1/genetics* ; Apoptosis/drug effects* ; Plant Extracts/therapeutic use* ; Doxorubicin

Rheumatoid arthritis (RA) represents a persistent autoimmune condition distinguished by a multifaceted etiology that encompasses both genetic and environmental factors. Recent progress in understanding the mechanisms behind RA pathogenesis has delved into the critical role of epigenetic regulatory processes, including DNA methylation, histone modifications, and the regulation by microRNAs (miRNAs). These findings provide new insights into the intricate nature of RA and pave the way for innovative therapeutic strategies. This review consolidates the latest developments in the epigenetic regulation of RA, concentrating on how these mechanisms affect the dysregulated signaling pathways associated with the disease. We analyze the roles of specific proteins that function as 'writers', 'erasers', and 'readers' in epigenetic modifications, highlighting their potential as targets for therapeutic intervention. Additionally, in view of the significance of miRNAs in the pathogenesis of RA, we deliberate on their involvement in disease progression and explore miRNA-based treatment strategies. By integrating these diverse epigenetic dimensions, this review offers a comprehensive epigenetic perspective on RA pathogenesis and identifies promising avenues for future research and therapeutic interventions.

Objective: To construct a nonsuicidal selfinjury (NSSI) risk prediction model for middle school students using different machine learning algorithms and evaluate the models effectiveness, so as to provide guidance for the prevention and control of NSSI in campus. Methods: In March 2023, a total of 3 372 middle and high school students from schools in Nanchang, Fuzhou and Shangrao cities in Jiangxi Province were selected by combining stratified random cluster sampling and convenient sampling methods. Questionnaire surveys were conducted using various instruments including general information questionnaire, Selfesteem Scale, Ottawa Selfinjury Scale, Social Support Assessment Scale, Chinese Version of the Olweus Bullying Questionnaire, Event Attribution Style Scale, Adolescent Resilience Scale, and Adolescent Life Events Scale. Data were divided into training set (n=2 361) and test set (n=1 011) at a ratio of 7∶3, and variables were selected based on univariate and LASSO regression results. Four machine learning algorithms including namely random forest, support vector machine, Logistic regression and XGBoost, were used to construct NSSI risk prediction models, and the models performance was evaluated and compared using metrics including area under curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value and F1 score. Results: The detection rate of NSSI among middle school students was 34.4%. Univariate analysis showed that there were statistically significant differences in NSSI detection rates among middle school students of different grades, genders, registered residence locations, whether they were class cadres and four types of bullying (physical, verbal, relational bullying and cyberbullying) (χ2=27.17, 15.81, 11.54, 4.63；68.22, 140.63, 77.81, 13.95, P<0.05). NSSI was included as the dependent variable in the LASSO regression model for variable screening, and the results regression identified 10 predictive variables including grade level, selfesteem, subjective support, support utilization, verbal bullying, emotional control, interpersonal relationships, punishment, loss of relatives and property, and health and adaptation issues. The AUC values of random forest, support vector machine, Logistic regression, and XGBoost algorithms were 0.76, 0.76, 0.76 and 0.77, respectively, with no statistically significant differences between pairwise comparisons (Z=-0.59-0.82, P>0.05). Sensitivity values were 0.62, 0.61, 0.62 and 0.61, respectively. Specificity values were 0.74, 0.78, 0.78 and 0.78, respectively. Positive predictive values were 0.56, 0.59, 0.60 and 0.59, respectively. Negative predictive values were 0.79, 0.79, 0.80 and 0.79, respectively. F1 scores were 0.59, 0.60, 0.61 and 0.60, respectively. Conclusions All four nonsuicidal selfinjury risk prediction models perform well, with the Logistic regression model slightly outperforming the others. Schools and parents should pay attention to the predictive factors corresponding to NSSI, so as to reduce the occurrence of NSSI among middle school students.

Circular RNA(circRNA)is an emerging class of endogenous non-coding RNA,which is widely expressed in the brain and plays an important role in a variety of biological processes.Research has shown that circRNA plays a key role in physiological and pathological processes of the brain,such as neurodevelopment,synaptic plasticity and neurodegenerative diseases through a variety of mecha-nisms such as adsorption of microRNA,binding to proteins and translation of peptides.In the field of drug addiction,the expression of circRNA is significantly changed in animal models and brains of addicts,and the regulation involves neural adaptation in brain regions that form the reward circuit such as the nucleus accumbens and prefrontal cortex.Additionally,addiction-related circRNAs are closely associated with neurotransmitter systems,signaling pathways,and neuroinflammatory responses,and they influ-ence the formation and maintenance of drug addiction by modulating gene expression networks related to drug addiction.Here,the biogenesis and regulatory mechanism of circRNA as well as its important role in brain function and drug addiction are reviewed in order to provide a new perspective for explora-tions of the pathological mechanism of drug addiction.

Objective:To develop a deep learning method to predict the anatomical images of nasopharyngeal carcinoma patients during the treatment course, which could detect the anatomical variation for specific patients in advance.Methods:Imaging data including planning CT (pCT) and cone-beam CT (CBCT) for each fraction of 230 patients with T 3-T 4 staging nasopharyngeal carcinoma who treated in Cancer Hospital Chinese Academy of Medical Sciences from January 1, 2020 to December 31, 2022 were collected. The anatomical images of week k+1 were predicted using a 3D Unet model with inputs of pCT, CBCT on days 1-3, and CBCT of weeks 2- k. In this experiment, we trained four models to predict anatomical images of weeks 3-6, respectively. The nasopharynx gross tumor volume (GTV nx) and bilateral parotid glands were delineated on the predicted and real images (ground truth). The performance of models was evaluated by the consistence of the delineation between the predicted and ground truth images. Results:The proposed method could predict the anatomical images over the radiotherapy course. The contours of interest in the predicted image were consistent with those in the real image, with Dice similarity coefficient of 0.96, 0.90, 0.92, mean Hausdorff distance of 3.28, 4.18 and 3.86 mm, and mean distance to agreement of 0.37, 0.70, and 0.60 mm, for GTV nx, left parotid, and right parotid, respectively. Conclusion:This deep learning method is an accurate and feasible tool for predicting the patient's anatomical images, which contributes to predicting and preparing treatment strategy in advance and achieving individualized treatment.

Objective To explore the risk factors of complicating urogenic sepsis after percutaneous nephrolithotripsy(PCNL)and construct a nomogram prediction model.Methods The data of 291 patients with stage 1 PCNL in 940 Hospital of Joint Logistics Support Force from October 2016 to October 2021 were retrospectively analyzed.The patients were divided into the sepsis group and non-sepsis group according to whether complicating urogenic sepsis after operation.The general data,stone-related data,operation-related data and laboratory detection related data were included.The independent risk factors were screened by the univariate and multivariate logistic regression analysis,and the nomogram prediction model was constructed.Results The results of univariate and multivariate logistic regression analysis showed that age≥60 years old(OR=6.438,95％CI:1.548-26.769),urinary leukocyte 3+(OR=5.651,95％CI:1.614-31.766),urinary nitrite positive(OR=7.117,95％CI:1.190-42.561),operation time≥90 min(OR=4.626,95％CI:1.137-18.817)and perfusion volume 30 L(OR=3.312,95％CI:1.090-10.061)were the independent risk factors of postoperative complicating urogenic sepsis.C-index of the constructed nomogram prediction model in the modeling samples was 0.937,the calibrated C-index was 0.914,and the model predictive efficien-cy was good.Conclusion Age ≥60 years old,urinary leukocyte 3+,urinary nitrite positive,operation time 90 min and perfusion volume ≥30 L are the independent risk factors for complicating urogenic sepsis after PCNL;the constructed nomogram prediction model has a good predictive efficiency for the occurrence of post-operative urogenic sepsis.

Zi goose is a small local variety with high fecundity,good meat quality,roughage resist-ance,strong adaptability and excellent down quality.It is an excellent female parent for cross breeding among varieties.With the rapid development of goose industry,the variety of Zi goose has not been well protected,the variety is hybrid and degraded seriously,and the number of pure Zi goose is decreasing day by day.This paper reviewed the research progress on the breeding distribu-tion and preservation status of Zi goose and the variety characteristics of Zi goose,in order to pro-vide reference for the research,protection and utilization of germplasm resources of Zi goose and the stable development of goose industry.

Objective To improve the performance of auto-segmentation of prostate target area and organs-at-risk in online magnetic resonance image and enhance the efficiency of magnetic resonance imaging-guided adaptive radiotherapy(MRIgART)for prostate cancer.Methods A retrospective study was conducted on 40 patients who underwent MRIgART for prostate cancer,including 25 in the training set,5 in the validation set,and 10 in the test set.The planning CT images and corresponding contours,along with online MR images,were registered and input into a deep learning network for online MR image auto-segmentation.The proposed method was compared with deformable image registration(DIR)method and single-MR-input deep learning(SIDL)method.Results The overall accuracy of the proposed method for auto-segmentation was superior to those of DIR and SIDL methods,with average Dice similarity coefficients of 0.896 for clinical target volume,0.941 for bladder,0.840 for rectum,0.943 for left femoral head and 0.940 for right femoral head,respectively.Conclusion The proposed method can effectively improve the accuracy and efficiency of auto-segmentation in MRIgART for prostate cancer.

Objective To explore the inhibitory effect of Sidaxue, a traditional Miao herbal medicine formula, on articular bone and cartilage destruction and synovial neovascularization in rats with collagen-induced arthritis (CIA). Methods In a SD rat model of CIA, we tested the effects of daily gavage of Sidaxue at low, moderate and high doses (10, 20, and 40 g/kg, respectively) for 21 days, with Tripterygium glycosides (GTW) as the positive control, on swelling in the hind limb plantar regions by arthritis index scoring. Pathologies in joint synovial membrane of the rats were observed with HE staining, and serum TNF-α and IL-1β levels were detected with ELISA. The expressions of NF-κB p65, matrix metalloproteinase 1 (MMP1), MMP2 and MMP9 at the mRNA and protein levels in the synovial tissues were detected using real-time PCR and Western blotting. Network pharmacology analysis was conducted to identify the important target proteins in the pathways correlated with the therapeutic effects of topical Sidaxue treatment for RA, and the core target proteins were screened by topological analysis. Results Treatment with GTW and Sidaxue at the 3 doses all significantly alleviated plantar swelling, lowered arthritis index scores, improved cartilage and bone damage and reduced neovascularization in CIA rats (P<0.05), and the effects of Sidaxue showed a dose dependence. Both GTW and Sidaxue treatments significantly lowered TNF-α, IL-1β, NF-κB p65, MMP1, MMP2, and MMP9 mRNA and protein expressions in the synovial tissues of CIA rats (P<0.05). Network pharmacological analysis identified MMPs as the core proteins associated with topical Sidaxue treatment of RA. Conclusion Sidaxue alleviates articular bone and cartilage damages and reduces synovial neovascularization in CIA rats possibly by downregulating MMPs via the TNF-α/IL-1β/NF-κB-MMP1, 2, 9 signaling pathway, and MMPs probably plays a key role in mediating the effect of Sidaxue though the therapeutic pathways other than oral administration.