RESUMO
BACKGROUND: Breast cancer (BC) is the most common type of cancer in women. Among many risk factors of BC, mutations in BRCA2 gene were found to be the primary cause in 5-10% of cases. The majority of deleterious mutations are frameshift or nonsense mutations. Most of the reported BRCA2 mutations are protein truncating mutations. METHODS: The study aimed to describe the pattern of mutations including single nucleotide polymorphisms (SNPs) and variants of the BRCA2 (exon11) gene among Sudanese women patients diagnosed with BC. In this study a specific region of BRCA2 exon 11 was targeted using PCR and DNA sequencing. RESULTS: Early onset cases 25/45 (55.6%) were premenopausal women with a mean age of 36.6 years. Multiparity was more frequent within the study amounting to 30 cases (66.6%), with a mean parity of 4.1. Ductal type tumor was the predominant type detected in 22 cases (48.8%) among the reported histotypes. A heterozygous monoallelic nonsense mutation at nucleotide 3385 was found in four patients out of 9, where TTA codon was converted into the stop codon TGA. CONCLUSION: This study detected a monoallelic nonsense mutation in four Sudanese female patients diagnosed with early onset BC from different families. Further work is needed to demonstrate its usefulness in screening of BC.
Assuntos
Alelos , Proteína BRCA2/genética , Neoplasias da Mama/genética , Polimorfismo de Nucleotídeo Único , Adulto , Idoso , Idoso de 80 Anos ou mais , Sequência de Aminoácidos , Sequência de Bases , Estudos de Casos e Controles , Códon sem Sentido , Éxons , Feminino , Heterozigoto , Humanos , Pessoa de Meia-Idade , Pré-Menopausa , Análise de Sequência de DNA , Sudão/epidemiologiaRESUMO
Magnesium alloys are widely used in aerospace vehicles and modern cars, due to their rapid machinability at high cutting speeds. A novel Edgeworthâ»Pareto optimization of an artificial neural network (ANN) is presented in this paper for surface roughness (Ra) prediction of one component in computer numerical control (CNC) turning over minimal machining time (Tm) and at prime machining costs (C). An ANN is built in the Matlab programming environment, based on a 4-12-3 multi-layer perceptron (MLP), to predict Ra, Tm, and C, in relation to cutting speed, vc, depth of cut, ap, and feed per revolution, fr. For the first time, a profile of an AZ61 alloy workpiece after finish turning is constructed using an ANN for the range of experimental values vc, ap, and fr. The global minimum length of a three-dimensional estimation vector was defined with the following coordinates: Ra = 0.087 µm, Tm = 0.358 min/cm³, C = $8.2973. Likewise, the corresponding finish-turning parameters were also estimated: cutting speed vc = 250 m/min, cutting depth ap = 1.0 mm, and feed per revolution fr = 0.08 mm/rev. The ANN model achieved a reliable prediction accuracy of ±1.35% for surface roughness.