BackgroundThe occurrence rate of dangerous behaviors in patients with severe mental disorders is higher than that of the general population. In China， there is limited research on the prediction of dangerous behaviors in community-dwelling patients with severe mental disorders， particularly in terms of predicting models using data mining techniques other than traditional methods. ObjectiveTo explore the influencing factors of dangerous behaviors in community-dwelling patients with severe mental disorders and testing whether the classification decision tree model is superior to the Logistic regression model. MethodsA total of 11 484 community-dwelling patients with severe mental disorders who had complete follow-up records from 2013 to 2022 were selected on December 2023. The data were divided into a training set （n=9 186） and a testing set （n=2 298） in an 8∶2 ratio. Logistic regression and classification decision trees were separately used to establish predictive models in the training set. Model discrimination and calibration were evaluated in the testing set. ResultsDuring the follow-up period， 1 115 cases （9.71%） exhibited dangerous behaviors. Logistic regression results showed that urban residence， poverty， guardianship， intellectual disability， history of dangerous behaviors， impaired insight and positive symptoms were risk factors for dangerous behaviors （OR=1.778， 1.459， 2.719， 1.483， 3.890， 1.423， 2.528， 2.124， P<0.01）. Being aged ≥60 years， educated， not requiring prescribed medication and having normal social functioning were protective factors for dangerous behaviors （OR=0.594， 0.824， 0.422， 0.719， P<0.05 or 0.01）. The predictive effect in the testing set showed an area under curve （AUC） of 0.729 （95% CI： 0.692~0.766）， accuracy of 70.97%， sensitivity of 59.71%， and specificity of 72.05%. The classification decision tree results showed that past dangerous situations， positive symptoms， overall social functioning score， economic status， insight， household registration， disability status and age were the influencing factors for dangerous behaviors. The predictive effect in the testing set showed an AUC of 0.721 （95% CI： 0.705~0.737）， accuracy of 68.28%， sensitivity of 64.46%， and specificity of 68.60%. ConclusionThe classification decision tree does not have a greater advantage over the logistic regression model in predicting the risk of dangerous behaviors in patients with severe mental disorders in the community. ［Funded by Chengdu Medical Research Project （number， 2020052）］

Background In vitro-in vivo extrapolation (IVIVE) is an approach utilizing in vitro experimental data to predict in vivo phenomena. It is a promising tool for chemical risk assessment. Objective To learn the hotspots, evolution path, and trend of IVIVE in risk assessment by literature search and bibliometric analysis, and provide reference and data support for subsequent research. Methods PubMed and Web of Science Core Collection were selected as foreign databases to search for literature about IVIVE applied in risk assessment published by December 31, 2023. The number of relevant documents in CNKI and Wanfang database was too small, so the Chinese databases were not included in this study. This study employed bibilometric analysis using VOSviewer and CiteSpace for visualizing networks categorized by author, institution, country, journal, keyword, and co-citation. Results A total of 189 articles were included in this study. The first article was published in 2006, and since then the number of publications overall showed an upward trend and increased significantly after 2016. The institution with the most publications was the United States Environmental Protection Agency (28 articles). The United States was the most productive country (87 articles), and had a close cooperation with the United Kingdom. The journal with the most publications and the highest number of citations per article was Archives of Toxicology (19 articles). The keyword co-occurrence analysis suggested that research on IVIVE in risk assessment mainly studied the methods and models of IVIVE and prediction of chemical toxicity, and toxicity, in vitro, and models were the research hotspots in this field. Keyword timeline cluster analysis suggested that the assessment objects gradually expanded from drugs to environmental chemicals, organic chemicals and food additives. The co-citation analysis suggested that articles about IVIVE in risk assessment mostly cited journals in the environment, food, and drug fields, and these articles were mainly methodological studies followed by literature reviews. Conclusion The research of IVIVE in risk assessment has developed rapidly. With the improvement of prediction models and the expansion of application scope, animal experiments in risk assessment may be greatly reduced and the efficiency of risk assessment can be increased. At present, the United States has a leading position in this field, while China has few relevant studies and needs to actively carry out international cooperation to improve the level of applied research of IVIVE. In the future, it is hoped that the IVIVE method can be further refined to improve its application and expand its research fields.

Objective To investigate the expression of PCI Domain Containing 2(PCID2)in gastric cancer,its effect on gastric cancer cell cycle and proliferation and the possible molecular mechanisms.Methods We examined PCID2 expression levels in gastric cancer and adjacent tissues from 100 patients undergoing radical gastrectomy in our hospital between January,2012 and December,2016,and analyzed the correlation of PCID2 expression level with cancer progression and postoperative 5-year survival rate of the patients.GO enrichment analysis was performed to identify the possible pathways that mediated the effect of PCID2 in gastric cancer progression.The effects of lentivirus-mediated PCID2 knockdown and overexpression on cell proliferation and cell cycle were analyzed in gastric cancer MGC-803 cells and in nude mice.Results PCID2 was highly expressed in gastric cancer tissues and positively correlated with peripheral blood levels of CA19-9 and CEA(P<0.01).In gastric cancer patients,a high PCID2 expression was associated with a significantly lowered postoperative 5-year survival rate(P<0.001)as an independent risk factor for postoperative survival(HR:2.987,95%CI:1.616-5.519).The sensitivity,specificity,and area under the curve of PCID2 for predicting postoperative 5-year survival were 76.74%,75.44%,and 0.755(P<0.001),respectively.GO enrichment analysis suggested that PCID2 was associated with gastric cancer cell cycle progression.PCID2 overexpression in MGC-803 cells significantly promoted cell proliferation,G1/S phase transition,expressions of cyclin D1 and CDK6,and the growth of transplanted xenograft in nude mice(P<0.05).The expressions of p27 and p16 were significantly lowered in gastric cancer tissues,and their expression levels were negatively regulated by PCID2 expression in MGC-803 cells(P<0.05).Conclusion PCID2 is highly expressed in gastric cancer tissues in close correlation with poor prognosis of the patients.High PCID2 expression promotes gastric cancer proliferation and cell cycle progression by inhibiting the expression of p27 and p16.

Objective To investigate the effects of kuwanon G(KG)on proliferation,apoptosis,migration and invasion of gastric cancer cells and the molecular mechanisms.Methods The effects of KG on proliferation and growth of gastric cancer cells were assessed with CCK-8 assay and cell clone formation assay,by observing tumor formation on the back of nude mice and using immunohistochemical analysis of Ki-67.The effect of KG on cell apoptosis was analyzed using Annexin V-FITC/PI apoptosis detection kit,Western blotting and TUNEL staining.The effects of KG on cell migration and invasion were detected using Transwell migration and invasion assay and Western blotting for matrix metalloproteinase(MMP).The role of phosphatidylinositol 3-kinase(PI3K)/protein kinase B(AKT)/mammalian target of rapamycin(mTOR)pathway in KG-mediated regulation of gastric cancer cell proliferation,migration,and invasion was verified by Western blotting and rescue assay.Results KG significantly inhibited proliferation and reduced clone formation ability of gastric cancer cells in a concentration-dependent manner(P<0.05).KG treatment also increased apoptosis,enhanced the expressions of cleaved caspase-3 and Bax,down-regulated Bcl-2,lowered migration and invasion capacities and inhibited the expression of MMP2 and MMP9 in gastric cancer cells(P<0.05).Mechanistic validation showed that KG inhibited the activation of the PI3K/AKT/mTOR pathway,and IGF-1,an activator of the PI3K/AKT/mTOR pathway,reversed the effects of KG on proliferation,migration and invasion of gastric cancer cells(P<0.05).Conclusion KG inhibits proliferation,migration and invasion and promotes apoptosis of gastric cancer cells at least in part by inhibiting the activation of the PI3K/AKT/mTOR pathway.

There are many kinds of zirconia repair materials that have been introduced at home and abroad.Mechanical and aesthetic properties are the important factors for selecting zirconia materials.The process of diagnosis and treatment by dentists and the production by the workers in laboratory affect the final repair effects.To achieve accurate and efficient simulation of dental repair and treatment,effective cooperation between dentists and technicians is required.In this paper,the factors affecting mechanical and aes-thetic properties in the process of material selection,tooth wearing,restoration design and fabrication,concerning zirconia veneer,sin-gle-crown,fixed bridge and edentulous jaw implant fixed repair were discussed and summarized.The common failure reasons were ana-lyzed in order to improve the communication efficiency between dentists and technicians in the process of zirconia repair system and to a-chieve better repair effects.

Objective To investigate the expression of PCI Domain Containing 2(PCID2)in gastric cancer,its effect on gastric cancer cell cycle and proliferation and the possible molecular mechanisms.Methods We examined PCID2 expression levels in gastric cancer and adjacent tissues from 100 patients undergoing radical gastrectomy in our hospital between January,2012 and December,2016,and analyzed the correlation of PCID2 expression level with cancer progression and postoperative 5-year survival rate of the patients.GO enrichment analysis was performed to identify the possible pathways that mediated the effect of PCID2 in gastric cancer progression.The effects of lentivirus-mediated PCID2 knockdown and overexpression on cell proliferation and cell cycle were analyzed in gastric cancer MGC-803 cells and in nude mice.Results PCID2 was highly expressed in gastric cancer tissues and positively correlated with peripheral blood levels of CA19-9 and CEA(P<0.01).In gastric cancer patients,a high PCID2 expression was associated with a significantly lowered postoperative 5-year survival rate(P<0.001)as an independent risk factor for postoperative survival(HR:2.987,95%CI:1.616-5.519).The sensitivity,specificity,and area under the curve of PCID2 for predicting postoperative 5-year survival were 76.74%,75.44%,and 0.755(P<0.001),respectively.GO enrichment analysis suggested that PCID2 was associated with gastric cancer cell cycle progression.PCID2 overexpression in MGC-803 cells significantly promoted cell proliferation,G1/S phase transition,expressions of cyclin D1 and CDK6,and the growth of transplanted xenograft in nude mice(P<0.05).The expressions of p27 and p16 were significantly lowered in gastric cancer tissues,and their expression levels were negatively regulated by PCID2 expression in MGC-803 cells(P<0.05).Conclusion PCID2 is highly expressed in gastric cancer tissues in close correlation with poor prognosis of the patients.High PCID2 expression promotes gastric cancer proliferation and cell cycle progression by inhibiting the expression of p27 and p16.

Objective To investigate the effects of kuwanon G(KG)on proliferation,apoptosis,migration and invasion of gastric cancer cells and the molecular mechanisms.Methods The effects of KG on proliferation and growth of gastric cancer cells were assessed with CCK-8 assay and cell clone formation assay,by observing tumor formation on the back of nude mice and using immunohistochemical analysis of Ki-67.The effect of KG on cell apoptosis was analyzed using Annexin V-FITC/PI apoptosis detection kit,Western blotting and TUNEL staining.The effects of KG on cell migration and invasion were detected using Transwell migration and invasion assay and Western blotting for matrix metalloproteinase(MMP).The role of phosphatidylinositol 3-kinase(PI3K)/protein kinase B(AKT)/mammalian target of rapamycin(mTOR)pathway in KG-mediated regulation of gastric cancer cell proliferation,migration,and invasion was verified by Western blotting and rescue assay.Results KG significantly inhibited proliferation and reduced clone formation ability of gastric cancer cells in a concentration-dependent manner(P<0.05).KG treatment also increased apoptosis,enhanced the expressions of cleaved caspase-3 and Bax,down-regulated Bcl-2,lowered migration and invasion capacities and inhibited the expression of MMP2 and MMP9 in gastric cancer cells(P<0.05).Mechanistic validation showed that KG inhibited the activation of the PI3K/AKT/mTOR pathway,and IGF-1,an activator of the PI3K/AKT/mTOR pathway,reversed the effects of KG on proliferation,migration and invasion of gastric cancer cells(P<0.05).Conclusion KG inhibits proliferation,migration and invasion and promotes apoptosis of gastric cancer cells at least in part by inhibiting the activation of the PI3K/AKT/mTOR pathway.

Objective:To investigate the feasibility of developing a radiomics model based on MRI and clinical features to predict the PD-L1 level in breast cancer.Methods:A total of 139 consecutive patients with breast cancer confirmed by pathology were enrolled retrospectively, including 79 PD-L1 negative patients and 60 PD-L1 positive patients. All patients were randomly assigned to a training dataset( n=97) and a validation dataset( n=42). Radiomics features were extracted from dynamic contrast-enhanced MRI. Radiomics feature selection was generated through the analysis of variance(ANOVA), least absolute shrinkage and selection operator(LASSO). Radiomics model and comprehensive model were developed for predicting the level of PD-L1. The receiver operating characteristic curve(ROC) was used to evaluate the predictive capacity of the models. Results:The radiomics model exhibited good performance in the training and validation datasets, with an area under the curve(AUC) of 0.847(95% confidence interval CI: 0.770-0.924) and 0.826(95% CI: 0.699-0.954), respectively. Compared with the radiomics model, the clinical feature combined prediction model showed better results, with AUC of 0.919(95% CI: 0.868-0.970) and 0.882(95% CI: 0.782-0.982), respectively, but without statistically significant difference( Z=1.32, P=0.19), respectively, but without statistically significant difference. Conclusions:The radiomi.Conclusions:The radiomics model has a certain value in preoperative prediction of PD-L1 expression level in breast cancer, which may be used as a supplement and improvement to the pathological gold standard to provide support for clinical decision-making.

The continuing discoveries of novel classes of RNA modifications in various organisms have raised the need for improving sensitive, convenient, and reliable methods for quantifying RNA modifications. In particular, a subset of small RNAs, including microRNAs (miRNAs) and Piwi-interacting RNAs (piRNAs), are modified at their 3'-terminal nucleotides via 2'-O-methylation. However, quantifying the levels of these small RNAs is difficult because 2'-O-methylation at the RNA 3'-terminus inhibits the activity of polyadenylate polymerase and T4 RNA ligase. These two enzymes are indispensable for RNA labeling or ligation in conventional miRNA quantification assays. In this study, we profiled 3'-terminal 2'-O-methyl plant miRNAs in the livers of rice-fed mice by oxidative deep sequencing and detected increasing amounts of plant miRNAs with prolonged oxidation treatment. We further compared the efficiency of stem-loop and poly(A)-tailed RT-qPCR in quantifying plant miRNAs in animal tissues and identified stem-loop RT-qPCR as the only suitable approach. Likewise, stem-loop RT-qPCR was superior to poly(A)-tailed RT-qPCR in quantifying 3'-terminal 2'-O-methyl piRNAs in human seminal plasma. In summary, this study established a standard procedure for quantifying the levels of 3'-terminal 2'-O-methyl miRNAs in plants and piRNAs. Accurate measurement of the 3'-terminal 2'-O-methylation of small RNAs has profound implications for understanding their pathophysiologic roles in biological systems.
Animals ; High-Throughput Nucleotide Sequencing ; Humans ; Methylation ; Mice ; MicroRNAs/genetics* ; Oxidative Stress ; RNA, Small Interfering/metabolism* ; Real-Time Polymerase Chain Reaction

Objective:To create radiomics models based on abbreviated multimodal magnetic resonance imaging (MRI) for the diagnosis of breast cancer.Methods:All breast MR imaging data between Jun 2014 and Mar 2019 were retrospectively collected. Patients with pathological results of puncture or surgical resection were involved in this study. One thousand three hundred and six patients (416 benign and 890 breast cancer) were divided into training cohort ( n=702), internal validation cohort ( n=302), and external validation cohort ( n=302). All images were reduced to: the joint model group [including T2 weighted imaging (T2WI), DWI (diffusion-weighted imaging) and first contrast-enhanced sequences], non-enhanced group (T2WI and DWI) and single-phase enhanced group (first contrast-enhanced sequences). Analysis of variance (ANOVA) and least absolute shrinkage and selection operator (LASSO) were used to reduce the dimension of texture features. Three supervised machine learning algorithms (Bagging decision tree, Gaussian process, support vector machine) were used to predict benign and malignant breast lesions, and the best classifier was selected to construct breast cancer diagnosis model. Models were validated by internal and external validation cohorts. Results:The Gaussian process algorithm was chosen. The area under the curve (AUC) of the joint model and the non-enhanced model for predicting breast cancer were 0.903 and 0.893 for the training cohort, 0.893 and 0.863 for the internal validation cohort, and 0.878 and 0.864 for the external validation cohort.Conclusions:The radiomics model based on abbreviated multimodal MRI can accurately diagnose breast cancer. And the non-enhanced model can accurately diagnose breast cancer without contrast enhancement, which provides feasibility for simplifying the diagnosis process.