看護観形成と臨地実習における今後の課題

本研究では、成人看護実習前後の学生に同意を得た44名を対象に、現時点での「看護に対する考え」をレポートとして記述してもらった。その結果から以下のことが明示された。1.学生が記述した看護観は、段階的には区分はできたが、深まりに困難性を感じた。2.患者との関わり場面において、その時々の学生の感じたことや具体的内容は読み取りにくい部分が認められた。3.学生の体験したことをその時々で討議し共有し、次のステップへ積み上げる体制づくりが必要である。そのため、実習中における学生の『看護観すくいあげ展開構造図』を示した。今後これを用いながら日々の学生への実習展開における学習指導過程の強化を図り、看護に対する考えを深化する関わりをしたい

成人看護実習における技術到達度の学生の認識

成人看護実習において学生が看護基本技術の到達度をどのように認識しているのかを明らかにすることを目的に実態調査をおこなった。その結果、1)日常生活の援助技術では「一人でできる」水準に達している学生が多かった。しかし、水準に達していない項目では繰り返し経験する必要性が示唆された。2)診療の補助技術では「経験なし」とする学生が多かったため、「見学」の機会を調整する必要性が示唆された。本研究により今後の成人看護技術教育への方法が示唆された

A Novel Serum Metabolomics-Based Diagnostic Approach for Colorectal Cancer

<div><h3>Background</h3><p>To improve the quality of life of colorectal cancer patients, it is important to establish new screening methods for early diagnosis of colorectal cancer.</p> <h3>Methodology/Principal Findings</h3><p>We performed serum metabolome analysis using gas-chromatography/mass-spectrometry (GC/MS). First, the accuracy of our GC/MS-based serum metabolomic analytical method was evaluated by calculating the RSD% values of serum levels of various metabolites. Second, the intra-day (morning, daytime, and night) and inter-day (among 3 days) variances of serum metabolite levels were examined. Then, serum metabolite levels were compared between colorectal cancer patients (N = 60; N = 12 for each stage from 0 to 4) and age- and sex-matched healthy volunteers (N = 60) as a training set. The metabolites whose levels displayed significant changes were subjected to multiple logistic regression analysis using the stepwise variable selection method, and a colorectal cancer prediction model was established. The prediction model was composed of 2-hydroxybutyrate, aspartic acid, kynurenine, and cystamine, and its AUC, sensitivity, specificity, and accuracy were 0.9097, 85.0%, 85.0%, and 85.0%, respectively, according to the training set data. In contrast, the sensitivity, specificity, and accuracy of CEA were 35.0%, 96.7%, and 65.8%, respectively, and those of CA19-9 were 16.7%, 100%, and 58.3%, respectively. The validity of the prediction model was confirmed using colorectal cancer patients (N = 59) and healthy volunteers (N = 63) as a validation set. At the validation set, the sensitivity, specificity, and accuracy of the prediction model were 83.1%, 81.0%, and 82.0%, respectively, and these values were almost the same as those obtained with the training set. In addition, the model displayed high sensitivity for detecting stage 0–2 colorectal cancer (82.8%).</p> <h3>Conclusions/Significance</h3><p>Our prediction model established via GC/MS-based serum metabolomic analysis is valuable for early detection of colorectal cancer and has the potential to become a novel screening test for colorectal cancer.</p> </div