Search Results

You are looking at 1 - 10 of 32 items for :

  • Human Biology x
Clear All
Open access

Mihaela Grigore, Sergiu Teleman, Didona Ungureanu and Alina Mares

. Schiffman M, Wentzensen N, Wacholder S, Kinney W, Gage JC, Castle PE. Human papillomavirus testing in the prevention of cervical cancer. J. Natl. Cancer Inst. 2011 Mar 2;103(5):368-83. 18. Cuzick J, Szarewski A, Cubie H, Hulman G, Kitchener H, Luesley D. Management of women who test positive for high-risk types of human papillomavirus: the HART study. Lancet. 2003; 362(9399):1871-6. 19. Wetnzensen N, Vinokurova S, von Knebel Doeberitz M. Systematic review of genomic integration sites of human papillomavirus genomes in epithelial dysplasia

Open access

Monica Licker, Roxana Moldovan, Elena Hogea, Delia Muntean, Florin Horhat, Luminița Baditoiu, Alexandru Florin Rogobete, Emil Tîrziu and Csilla Zambori

Abstract

The term biofilm designates an aggregate of microorganisms belonging to one or more species which adhere to various surfaces but also to each another. These microbial communities are included and interconnected within an organic structure known as slime, composed of protein substances, polysaccharides, and DNA.

The Center for Disease prevention and control considers infections with bacteria in biofilms among the 7 most important challenges which must be overcome in order to improve the safety of health services. The risk of microbial biofilm development exists for a long list of medical devices and equipment, as well as in certain diseases such as cystic fibrosis. An aggravating aspect is represented by the almost 1,000 times higher antimicrobial resistance of bacteria growing and multiplying within biofilms. Thus, in case of biofilm-infected medical devices, the resistance to antimicrobial treatments requires the removal of the device which essentially means the failure of the exploratory or therapeutic intervention in question.

The role of microbial biofilms in medical pathology is a subject that raises interest for both researchers and clinicians in order to establish new methods for prevention and treatment of biofilms. This paper is intended as an overview in the management of microbial biofilms, presenting future insights, with technological progress in microscopy, molecular genetics, and genome analysis. Therefore the present paper will focus on describing the mechanisms involved in biofilm development, biofilm related infections, methods of detection and quantification of microbial communities and therapeutical approaches.

Open access

Li-Wei Gao and Guo-Liang Wang

Abstract

Lung cancer (LC), which includes small-cell lung carcinoma (SCLC) and non-small-cell lung carcinoma (NSCLC), is common and has a high fatality rate. This study aimed to reveal the prognostic mechanisms of LC. GSE30219 was extracted from the Gene Expression Omnibus (GEO) database, and included 293 LC samples and 14 normal lung samples. Differentially expressed genes (DEGs) were identified using the Limma package, and subjected to pathway enrichment analysis using DAVID. MicroRNAs (miRNAs) targeting the DEGs were predicted using Webgestalt. Cytoscape software was used to build a protein-protein interaction (PPI) network and to identify significant network modules. Survival analysis was conducted using Survminer and Survival packages, and validation was performed using The Cancer Genome Atlas (TCGA) dataset. The good and poor prognosis groups contained 518 DEGs. miR-190, miR-493, and miR-218 for the upregulated genes and miR-302, miR-200, and miR-26 for the downregulated genes were predicted. Three network modules (module 1, 2, and 3) were identified from the PPI network. CDK1, MCM10, and NDC80 were the core nodes of module 1, 2, and 3, respectively. In module 1, CDK1 interacted with both CCNB1 and CCNB2. Additionally, CDK1, CCNB1, CCNB2, MCM10, and NDC80 expression levels correlated with clinical survival and were identified as DEGs in both GSE30219 and the TCGA dataset. miR-190, miR-493, miR-218, miR-200, and miR-302 might act in LC by targeting the DEGs. CDK1, CCNB1, CCNB2, MCM10, and NDC80 might also influence the prognosis of LC.

Open access

Nicoleta P. Berbec, Sorina M.F. Papuc, Andreea C.D.F. Tutulan-Cunita, Silvana M. Angelescu, Anca I. Lupu and Aurora A. Arghir

Abstract

De novo acute myeloid leukemias (AML) represent a heterogeneous group of clonal hematopoietic disorders in which chromosomal abnormalities are detected in a majority of patients. At present, cytogenetic changes are recognized as important diagnostic markers and prognosis determinants. Complex karyotype changes are associated with resistance to treatment and unfavorable evolution. We report on an AML case with complex karyotype changes characterized by molecular genetic techniques (fluorescence in situ hybridization - FISH and array-based comparative genomic hybridization - array-CGH) and an extremely poor outcome. A 72 year-old female patient was admitted for genetic investigations with a clinical diagnosis of AML. Classical and molecular cytogenetic tests as well as array-CGH were performed. Complex chromosomal abnormalities were identified at diagnosis, consisting of genomic imbalances involving chromosomes 6, 7, 9, and 17. AML with complex karyotype changes is a heterogeneous disease, as a variety of genomic abnormalities are detected, involving virtually all chromosomes. The pathogenesis of AML with complex karyotype is poorly understood. The complexity of karyotypic changes in our case highlights the importance of using complementary genetic investigation in order to obtain a comprehensive view of AML genome.

Open access

Roxana I. Serban, Iuliana Radu and Irina Codita

:170-178. 20. Olmer D, Olmer J. Répartition géographique actuelle de la fièvre boutonneuse. Mars Med. 1957, 8:525-536. 21. Raoult D, Kohler J L, Gallais H, de Micco P, Rousseau S, Casanova P. Fatal rickettsiosis (letter). Nouv Presse Med. 1982, 11(8):607. 22. Fournier PE, El Karkouri K, Leroy Q, Robert C, Giumelli B, Renesto P et al. Analysis of the Rickettsia africae genome reveals that virulence acquisition in Rickettsia species may be explained by genome reduction. BMC. 2009, 10:166-181. 23. Raport pentru

Open access

Simona Dumitriu, Enriko Klootwijk, Naomi Issler, Horia Stanescu, Robert Kleta and Maria Puiu

. 2011. 7-15-2013. Ref Type: Electronic Citation 18. USCS genome browser. http://genome.ucsc.edu/cgibin/hgBlat?command=start . 2009. 7-15-2013. Ref Type: Electronic Citation 19. USCSdbSNP(137). http://genome.ucsc.edu/cgibin/hgTrackUi?hgsid=342271035&c=chrX&g=snp137- Common . 2012. Ref Type: Electronic Citation 20. Ohki I, Shimotake N, Fujita N, Nakao M, Shirakawa M (1999). Solution structure of the methyl-CpG-binding domain of the methylation-dependent transcriptional repressor MBD1. EMBO J 18: 6653

Open access

Daniel Coriu, Dumitru Jardan, Cerasela Jardan, Rodica Tălmaci, Mihaela Dragomir and Anca Coliţă

References 1. Vardiman JW, Thiele J, Arber DA, Brunning RD, Borowitz RD, Porwit A, et al. The 2008 revision of the World Health Organization (WHO) classification of myeloid neoplasms and acute leukemia: rationale and important changes. Blood. 2009 July 30;114(5):937-951. DOI: 10.1182/blood-2009-03-209262 2. Ley TJ, Mardis ER, Ding L, Fulton B, McLellan MD, Chen K, et al. DNA sequencing of a cytogenetically normal acute myeloid leukaemia genome. Nature. 2008 Nov 6;456(7218):66-72. DOI: 10.1038/nature07485 3. Ding

Open access

Dragos Florea, Steliana Huhulescu, Alexander Indra, Ioana Badicut, Alexandru Rafila, Dan Otelea and Gabriel Adrian Popescu

hypervirulent strain, polymerase chain reaction ribotype 078. Clin Infect Dis. 2008;47:1162-70. DOI: 10.1086/592257 4. Stabler RA, He M, Dawson LFT, Martin M, Valiente E, Parkhill J, et al Comparative genome and phenotypic analysis of Clostridium difficile 027 strains provides insight into the evolution of a hypervirulent bacterium. Genome Biol. 2009;10:R102. DOI: 10.1186/gb-2009-10-9-r102 5. Lim SK, Stuart RL, Mackin KE, Carter GP, Kotsanas D, Francis MJ, et al. Emergence of a ribotype 244 strain of Clostridium difficile associated with severe disease and

Open access

Ioana Brudașcă and Mircea Cucuianu

References 1. Kraja AT, Vaidya D, Pankow JS, Goodarzi MO, Assimes TL, Kullo IJ, et al. A bivariate genome-wide approach to metabolic syndrome: STAMPEED consortium. Diabetes. 2011;60:1329-39. DOI: 10.2337/db10-1011. 2. Bruce KD, Hanson MA. The developmental origins, mechanisms, and implications of metabolic syndrome. J Nutr. 2010;140(3):648-52. DOI: 10.3945/ jn.109.111179. 3. Laker RC, Wlodek ME, Connelly JJ, Yan Z. Epigenetic origins of metabolic disease: The impact of the maternal condition to the offspring

Open access

Cristina Rusu, Adriana Sireteanu, Lăcrămioara Butnariu, Monica Pânzaru, Elena Braha, Doina Mihăilă and Roxana Popescu

;32(3):129-34. 15. White SJ, den Dunnen JT. Copy number variation in the genome; the human DMD gene as an example. Cytogenet Genome Res. 2006;115(3-4):240-6. DOI: 10.1159/000095920 16. Yan J, Feng J, Buzin CH, Scaringe W, Liu Q, Mendell JR, et al. Three-tiered noninvasive diagnosis in 96% of patients with Duchenne muscular dystrophy (DMD). Hum Mutat. 2004;23(2):203-4. DOI: 10.1002/ humu.10307 17. Dent KM, Dunn DM, von Niederhausern AC, Aoyagi AT, Kerr L, Bromberg MB, et al. Improved molecular diagnosis of dystrophinopathies in an unselected