RESUMO INTRODUÇÃO:A morte materna é um grande desafio à saúde pública brasileira; uma das principais formas de prevenção destas é o pré-natal, disponibilizado pela rede pública de saúde. Para que este ocorra com qualidade existem procedimentos básicos a serem realizados. OBJETIVOS: Atribuições do enfermeiro no atendimento pré-natal, com destaque para a assistência qualificada e as competências essenciais para o exercício da obstetrícia dos profissionais de enfermagem. METODOLOGIA: Trata-se de um levantamento bibliográfico. As bases consultadas foram MEDLINE, LILACS e SciELO, utilizando os descritores: pré-natal, mortalidade materna, competência profissional e enfermagem obstétrica. RESULTADOS: Verificamos que o papel do enfermeiro é de grande relevância no que diz respeito às ações voltadas ao pré-natal, pois sem ele, as consultas se restringem a um modelo biomédico. Neste contexto, faz-se necessário um conjunto de profissionais qualificados e habilitados para realizar o pré-natal, e consequentemente reduzir os números de morte materna. Palavras-chave: Cuidado Pré-Natal. Mortalidade Materna. Competência Profissional. Enfermagem Obstétrica. ABSTRACT INTRODUCTION:Maternal mortality is a great challenge to Brazilian public health; a of the main ways to prevent these is the prenatal care, provided by the public health. For this to occur with quality there are basic procedures to be performed. GOALS: Nurses' assignments in prenatal care, with emphasis on quality care and the essential skills for the exercise of obstetrics nurses. METHODOLOGY: This is a literature review. The following databases were MEDLINE, LILACS and SciELO, using the key words: prenatal, maternal mortality, professional competence and midwifery. RESULTS: We found that the role of nurses is of _______________________
Background Most colorectal cancers (CRC) arise from precursor lesions. This study aimed to characterize the mutation profile of colorectal cancer precursor lesions in a Brazilian population. Methods In total, 90 formalin-fixed paraffin-embedded colorectal precursor lesions, including 67 adenomas, 7 sessile serrated lesions, and 16 hyperplastic polyps, were analyzed by next-generation sequencing using a panel of 50 oncogenes and tumor suppressor genes. The genetic ancestry of the patients was estimated. Results Somatic driver mutations were identified in 66.7% of cases, including alterations in APC (32.2%), TP53 (20.0%), KRAS (18.9%), BRAF (13.3%) and EGFR (7.8%). Adenomas displayed a higher number of mutations, mainly in APC, compared to serrated polyps (73.1% vs. 47.8%, p = 0.026). Advanced adenomas had a significantly higher frequency of mutation in KRAS and a high overall mutation rate than early adenomas (92.9% vs. 59%, p = 0.006). A high degree of ancestry admixture was observed in the population studied, with a predominance of European components (mean of 73%) followed by African (mean of 11.3%). No association between genetic ancestry and type of lesions was found. The mutation profile of Brazilian colorectal precursor lesions exhibits alteration in APC, KRAS, TP53, and BRAF at different frequencies according to lesion type. Conclusions These results bestow the knowledge of CRC's biologic history and support the potential of these biomarkers for precursor lesions detection in CRC screening of the Brazilian population.
Background: Body condition score is used widely in swine production to ensure adequate nutritional levels in sows during gestation and lactation. However, body condition score is not a gold standard for the estimation of nutritional requirements in sows. Post-farrowing sow body weight assessment might serve as a useful approach for the better adjustment of the nutritional requirements during lactation; however, this approach is time-consuming, requires labor, and might result in detrimental effects on the sow behavior and welfare. The objective of the present study, therefore, was to formulate prediction equations for the estimation of post-farrowing sow weight.Materials, Methods & Results: Seven equations were formulated for predicting the post-farrowing sow body weight, by using the data from three databases, which comprised a total 522 sows (434 gilts and 88 multiparous). The sows were weighed on Day 112 of gestation and after farrowing within 12 h. The piglets birth weight was recorded within 24 h after farrowing. The equations were formulated considering all the parity orders. While formulating the equations, the following five variables were used: pre-farrowing body weight, piglets born, litter weight, the interval between pre-farrowing weighing and farrowing (in days), and the total feed intake between pre-farrowing and post-farrowing weighing. The seven models were compared using the sets of possible predictors through regression with the best subsets procedure (Minitab for Windows, v. 18). Equations (EQ) 1, 2, and 4 were validated with a database comprising 732 sows (parity orders: 1–5). The females were weighed on Day 107 of gestation and within 24 h after farrowing. The predicted weights estimated by EQ 2 and 4 (215.4 ± 34.3 kg and 216.7 ± 34.4 kg, respectively) did not significantly differ from the observed weight (216.8 ± 34.6 kg) [P > 0.05].Discussion: Pre-farrowing sow body weight was identified as the main input variable required for the estimation of the post-farrowing sow body weight. Thus, even EQ 1, which contained only this variable, exhibited a high coefficient of determination (R2 = 0.8707). However, the R2 value kept increasing as more input variables were included in the equation. Equation 2, 4, and 6 included the litter weight variable, and the addition of this variable increased the numerical value of R2 from 0.8707 in EQ 1 to 0.8975 in EQ 2. The EQ 3, 5, and 7 considered the piglets born variable as well, which increased the R2 value from 0.8707 in EQ 1 to 0.9119 in EQ 3. The coefficient of determination did not vary much among the equations; therefore, the selection of the prediction equations depended on data availability, feed management, facility, and the reliability of data collection in each farm. Although EQ 1 demonstrated a greater correlation between the predicted and the observed post-farrowing weight compared to the other equations, the values of error in central tendency and the errors due to disturbances were numerically higher for EQ 1 in comparison to the other two equations (EQ 2 and 4). Therefore, it is suggested that EQ 1 should be used as the last choice for the estimation of post-farrowing sow weight as it presented low trueness and precision, and also because the predicted weight estimated by EQ 1 was statistically lower than the observed weight (211.67 ± 33.33 kg vs. 216.84 ± 34.62 kg; P = 0.012). EQ 4 emonstrated higher trueness and precision; however, it did not differ significantly from EQ 2 and 1. Further analyses are required in order to validate EQ 3, 5, 6, and 7. Among the equations that were predicted as well as validated, the simplest and the easiest equation with satisfactory results for trueness and precision was EQ 2, which is as follows:Post-farrowing sow weight (kg) = 13.03 + (0.93 × pre-farrowing body weight, kg) + (–1.23 × piglets born, n)
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