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1.
Zhongguo Yi Xue Ke Xue Yuan Xue Bao ; 43(4): 590-594, 2021 Aug.
Artigo em Chinês | MEDLINE | ID: mdl-34494531

RESUMO

Objective To investigate the oral health status and awareness of urban children in Lhasa,aiming to provide a data basis for the prevention and treatment of children's caries and the promotion of oral health education. Methods A total of 504 Tibetan students were selected by cluster sampling from 2 primary schools in Chengguan District of Lhasa.All the participants were required to take oral health examination and complete a questionnaire about oral health awareness and behavior. Results The caries prevalence rate and mean decayed-missing-filled tooth(DMFT)of permanent teeth were 75.00% and 2.18±1.91,respectively.The rates of pit and fissure sealant and filling of permanent teeth were 3.77% and 6.81%,respectively.The caries prevalence rate of first permanent molars was 47.62%.The mean DMFT of permanent teeth and caries prevalence rate of first permanent molar were significantly higher in female group(P=0.001 and P=0.007,respectively).The prevalence rate of dental fluorosis was 61.51%,and the detection rate of dental calculus was 71.83%.Multivariate logistic regression analysis showed that prevalence of caries was influenced by many independent factors including gender,oral health awareness,intention of dental intervention,and dental experience. Conclusion High caries prevalence rate,low filling rate,and poor oral hygiene and health awareness were found among the primary school students in Lhasa,which require continuous dentistry investment and oral health education for the local students and their parents.


Assuntos
Cárie Dentária , Saúde Bucal , Criança , Índice CPO , Cárie Dentária/epidemiologia , Feminino , Humanos , Higiene Bucal , Prevalência , Instituições Acadêmicas , Estudantes , Inquéritos e Questionários
2.
Hua Xi Kou Qiang Yi Xue Za Zhi ; 38(6): 687-691, 2020 Dec 01.
Artigo em Chinês | MEDLINE | ID: mdl-33377348

RESUMO

The application of artificial intelligence in medicine has gradually received attention along with its development. Many studies have shown that machine learning has a wide range of applications in stomatology, especially in the clinical diagnosis and treatment of maxillofacial cysts and tumors. This article reviews the application of machine learning in maxillofacial cyst and tumor to provide a new method for the diagnosis of oral and maxillofacial diseases.


Assuntos
Cistos , Medicina Bucal , Inteligência Artificial , Cistos/diagnóstico , Humanos , Aprendizado de Máquina
3.
Front Oncol ; 10: 222, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32161722

RESUMO

Being the second most common type of primary bone malignancy in children and adolescents, Ewing Sarcoma (ES) encounters the dilemma of low survival rate with a lack of effective treatments. As an emerging approach to combat cancer, RNA therapeutics may expand the range of druggable targets. Since the genome-derived oncolytic microRNA-34a (miR-34a) is down-regulated in ES, restoration of miR-34a-5p expression or function represents a new therapeutic strategy which is, however, limited to the use of chemically-engineered miRNA mimics. Very recently we have developed a novel bioengineering technology using a stable non-coding RNA carrier (nCAR) to achieve high-yield production of biocompatible miRNA prodrugs, which is a great addition to current tools for the assessment of RNA therapeutics. Herein, for the first time, we investigated the biochemical pharmacology of bioengineered miR-34a-5p prodrug (nCAR/miR-34a-5p) in the control of ES using human ES cells and xenograft mouse models. The bioengineered nCAR/miR-34a-5p was precisely processed to mature miR-34a-5p in ES cells and subsequently suppressed cell proliferation, attributable to the enhancement of apoptosis and induction of G2 cell cycle arrest through downregulation of SIRT-1, BCL-2 and CDK6 protein levels. Furthermore, systemic administration of nCAR/miR-34a-5p dramatically suppressed the ES xenograft tumor growth in vivo while showing biocompatibility. In addition, the antitumor effect of bioengineered nCAR/miR-34a-5p was associated with a lower degree of tumoral cell proliferation and greater extent of apoptosis. These findings demonstrate the efficacy of bioengineered miR-34a-5p prodrug for the treatment of ES and support the development of miRNA therapeutics using biocompatible bioengineered miRNA prodrugs.

4.
BMC Genomics ; 20(Suppl 13): 980, 2019 Dec 27.
Artigo em Inglês | MEDLINE | ID: mdl-31881832

RESUMO

BACKGROUND: The three-dimensional (3D) structure of chromatins plays significant roles during cell differentiation and development. Hi-C and other 3C-based technologies allow us to look deep into the chromatin architectures. Many studies have suggested that topologically associating domains (TAD), as the structure and functional unit, are conserved across different organs. However, our understanding about the underlying mechanism of the TAD boundary formation is still limited. RESULTS: We developed a computational method, TAD-Lactuca, to infer this structure by taking the contextual information of the epigenetic modification signals and the primary DNA sequence information on the genome. TAD-Lactuca is found stable in the case of multi-resolutions and different datasets. It could achieve high accuracy and even outperforms the state-of-art methods when the sequence patterns were incorporated. Moreover, several transcript factor binding motifs, besides the well-known CCCTC-binding factor (CTCF) motif, were found significantly enriched on the boundaries. CONCLUSIONS: We provided a low cost, effective method to predict TAD boundaries. Above results suggested the incorporation of sequence features could significantly improve the performance. The sequence motif enrichment analysis indicates several gene regulation motifs around the boundaries, which is consistent with TADs may serve as the functional units of gene regulation and implies the sequence patterns would be important in chromatin folding.


Assuntos
Histonas/química , Redes Neurais de Computação , Algoritmos , Área Sob a Curva , Cromatina/metabolismo , Código das Histonas , Histonas/metabolismo , Ligação Proteica , Curva ROC
5.
Protein Pept Lett ; 18(9): 906-11, 2011 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-21529343

RESUMO

Protein-protein interactions (PPIs) are crucial to most biochemical processes in human beings. Although many human PPIs have been identified by experiments, the number is still limited compared to the available protein sequences of human organisms. Recently, many computational methods have been proposed to facilitate the recognition of novel human PPIs. However the existing methods only concentrated on the information of individual PPI, while the systematic characteristic of protein-protein interaction networks (PINs) was ignored. In this study, a new method was proposed by combining the global information of PINs and protein sequence information. Random forest (RF) algorithm was implemented to develop the prediction model, and a high accuracy of 91.88% was obtained. Furthermore, the RF model was tested using three independent datasets with good performances, suggesting that our method is a useful tool for identification of PPIs and investigation into PINs as well.


Assuntos
Algoritmos , Mapeamento de Interação de Proteínas/métodos , Proteínas/metabolismo , Bases de Dados de Proteínas , Humanos , Redes e Vias Metabólicas , Modelos Biológicos , Análise de Sequência de Proteína/métodos
6.
Protein Pept Lett ; 18(5): 450-6, 2011 May.
Artigo em Inglês | MEDLINE | ID: mdl-21171945

RESUMO

B-factor from X-ray crystal structure can well measure protein structural flexibility, which plays an important role in different biological processes, such as catalysis, binding and molecular recognition. Understanding the essence of flexibility can be helpful for the further study of the protein function. In this study, we attempted to correlate the flexibility of a residue to its interactions with other residues by representing the protein structure as a residue contact network. Here, several well established network topological parameters were employed to feature such interactions. A prediction model was constructed for B-factor of a residue by using support vector regression (SVR). Pearson correlation coefficient (CC) was used as the performance measure. CC values were 0.63 and 0.62 for single amino acid and for the whole sequence, respectively. Our results revealed well correlations between B-factors and network topological parameters. This suggests that the protein structural flexibility could be well characterized by the inter-amino acid interactions in a protein.


Assuntos
Maleabilidade , Análise de Sequência de Proteína/métodos , Estatística como Assunto/métodos , Biologia Computacional , Cristalografia por Raios X , Modelos Moleculares , Conformação Proteica , Mapeamento de Interação de Proteínas/métodos , Reprodutibilidade dos Testes
7.
Angew Chem Int Ed Engl ; 48(21): 3817-20, 2009.
Artigo em Inglês | MEDLINE | ID: mdl-19373817

RESUMO

Hooking up: FeCl(2) catalyzes the efficient cross dehydrogenative arylation of substrates having benzylic C-H bonds (see scheme). High regioselectivity was observed during the cross-coupling between compounds containing aromatic C(sp(2))-H bonds and benzylic C(sp(3))-H bonds. This process is proposed to proceed by single-electron-transfer oxidation and Friedel-Crafts alkylation.

8.
Interdiscip Sci ; 1(4): 315-9, 2009 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-20640811

RESUMO

Machine learning methods play the very important role in protein secondary structure prediction and other related works. On condition of a certain approach, the prediction qualities mostly depend on the ways of representing protein sequences into numeric features. In this paper, two Support Vector Machine (SVM) multi-classification strategies, "one-against-one" (1-a-1) and "one-against-all" (1-a-a), were used in protein structural classes identification. Auto covariance (AC), which transforms the physicochemical properties of the amino acids of the proteins into a data matrix, focuses on the neighboring effects and the interactions between residues in protein sequences. "1-a-1" approach was used on SVM to predict protein structural classes and obtained very promising overall accuracy 90.69% by Jackknife test. It was more than 10% higher than the accuracy obtained by using "1-a-a". Experimental results led to the finding that the SVM predictor constructed by "1-a-1" can avoid the appearance of biased prediction accuracy. This current method, using the protein primary sequence information described by auto covariance (AC) and "1-a-1" approach on SVM, should play an important complementary role in other related applications.


Assuntos
Inteligência Artificial , Biologia Computacional/métodos , Proteínas/química , Proteínas/classificação , Algoritmos , Simulação por Computador , Vetores Genéticos , Reconhecimento Automatizado de Padrão/métodos , Estrutura Secundária de Proteína , Reprodutibilidade dos Testes , Análise de Sequência de Proteína/métodos , Software
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