um cuidado especializado do enfermeiro obstetra

Observa-se, hoje em dia, que algumas práticas na maternidade tendem a ignorar as preferências das mulheres em trabalho de parto, uniformizando os cuidados com prejuízo para o bem-estar e a qualidade de vida das famílias. As práticas em Obstetrícia têm vindo a tornar-se cada vez mais repletas de intervenção, focando-se apenas nos resultados físicos (mortalidade e morbilidade) e descurando as vivências das parturientes e família, assim como as consequências psicossociais de um parto traumático. O presente Relatório de Estágio pretende refletir os cuidados em maternidade na perspetiva EEESMOG, que se visa holística, centrada no cliente e baseada na evidência. Da mesma forma, espelha as aprendizagens efetuadas em contexto do Estágio com Relatório inserido no 6º CMESMO da ESEL. Foram escolhidos como referenciais teóricos norteadores os modelos de Nola Pender – Modelo de Promoção da Saúde, e a Teoria de Empowerment em Saúde de Nelma Shearer. Foi também realizada uma Revisão Sistemática da Literatura que visou responder à seguinte questão de investigação: “Quais os cuidados do EEESMOG promotores do empowerment das mulheres direcionado para uma tomada de decisão informada relativa ao trabalho de parto?”. Adicionalmente, foi efetuado um registo da interação durante a prestação de cuidados no decorrer do estágio, sobre os quais foi efetuada uma reflexão e confrontação com os resultados da RSL. Concluiu-se que os cuidados que o EEESMOG presta que são promotores de uma tomada de decisão informada para o trabalho de parto se inserem dentro de três grandes temas, nomeadamente Competências da esfera relacional, Competências da esfera da prática clínica e Competências da esfera científica, com especial referência para os cuidados que se relacionam com o Estabelecimento de Relação Terapêutica, a Educação para a Saúde, o Cuidado da Mulher em trabalho de parto, a Promoção do exercício do Consentimento Informado e a Prática baseada na Evidência

Methods used to identify genes with methylation-expression correlations.

<p>(A) Pearson correlation was used to measure linear relationships between DNA methylation and gene expression levels for 1505 CpG probes represented on the GoldenGate Methylation BeadArray. The panels represent examples of a gene with high (left) and low (right) Pearson correlation coefficients when analyzing DNA methylation levels (x axis) against gene expression levels (y axis). (B) A discretization approach was used to classify samples into <i>methylated</i> (M) or <i>unmethylated</i> (U) groups based on the mean (<i>μ</i>) methylation value and standard deviation (<i>σ</i>) of a given probe. Statistically significant gene expression differences between M and U groups indicated a methylation-expression correlation for the gene in question.</p

A diagrammatic representation of <i>sample class</i> enrichments for selected genes.

<p>Samples classified as M or U by the discretization approach are indicated by green and blue bars respectively. The genomic context of samples in each group are shown as white, black or red bars. A representative locus is shown for p16 (A), DLC1 (B), IGF1R (C) and IL17RB (D).</p

Relationship to CpG islands and TSS for genes with methylation-expression correlations identified by computing a Pearson correlation coefficient.

*<p>Hypergeometric <i>P</i> values are given.</p

Validation of methylation-expression relationships by Pearson correlation for p16, DLC1, IGF1R and IL17RB using pyrosequencing and qRT-PCR in an independent sample set.

<p>Scatter plots depict the extent of linear correlation of DNA methylation (x axis) to gene expression (y axis) for p16 (A), DLC1 (B), IGF1R (C), IL17RB (D) in 43 MM samples. Methylation data was generated by bisulfite pyrosequencing and the average percent methylation for all CpG loci interrogated is shown. Gene expression relative fold-change was obtained by qRT-PCR. Pearson correlation coefficients are shown and <i>P</i> values are generated by random permutation tests.</p

Top 40 methylation loci with statistically significant methylation-expression correlations using the <i>discretization approach</i>.

<p>Additionally, loci in common with the Pearson correlation method are shown in parentheses.</p>*<p>(−) indicates a negative methylation-expression correlation, where high methylation correlates to lower expression or vice versa. (+) indicates a positive correlation where high methylation correlates to increased expression or vice versa.</p>†<p>P values were obtained after 10,000 random permutations of each class label.</p>‡<p>Gene was selected for further validation.</p>§<p>The methylation level of the probe FRZB_P406_F has a positive correlation with gene expression by both Pearson correlation and the discretization method, while the other probe FRZB_E186_R has a negative methylation-expression correlation by both methods. Only the FRZB_P406_F probe is shown for the discretization method due to its statistical significance.</p

Comparison of Pearson and discretization approaches used to identify methylation-expression correlations.

<p>The Venn Diagram displays the total number of overlapping loci with positive/negative methylation-expression correlations identified by computing either a Pearson correlation or applying the discretization method.</p

Summary of assay details for CpG sites interrogated by bisulfite pyrosequencing.

<p>Summary of assay details for CpG sites interrogated by bisulfite pyrosequencing.</p

CpG loci with statistically significant methylation-expression relationships as determined by Pearson correlation.

*<p>Average correlation is shown for loci with more than one gene expression probe.</p

Relationship to CpG islands and TSS for genes with methylation-expression correlations identified by the <i>discretization approach</i>.

*<p>Hypergeometric <i>P</i> values are given.</p