妊娠晚期孕妇抑郁情绪对新生儿神经行为发育的影响

目的探讨妊娠晚期孕妇抑郁情绪对新生儿神经行为发育的影响。方法选取2017年1月—2018年12月,收治本院的160例妊娠晚期孕妇抑郁情绪患者一般临床资料,妊娠周期28周以上,对孕妇抑郁情绪进行统计采用爱丁堡抑郁自评量表、负性认知加工偏向量表,对新生儿神经行为进行评估时按照婴儿神经国际量表。结果160例妊娠晚期孕妇抑郁情绪患者中,阳性30例、阴性130例,与阴性妊娠妇女相比,抑郁阳性妊娠妇女负性沉思偏向、负性解释偏向、负性记忆偏向、负性注意偏向均显著较高,差异具有统计学意义(P<0.05)。160例新生儿中,婴儿神经国际量表评分处于临界值为60例,正常值为100例,神经行为异常发生率为37.50%,负性沉思偏向、负性注意偏向与婴儿神经国际临界值呈负相关关系(P<0.05);与抑郁自评量表无明显相关性(P>0.05)。结论妊娠晚期孕妇具有较高抑郁发生率,其中负性认知加工偏向中负性沉思偏向、负性注意偏向与新生儿神经行为发育具有明显相关性。     

《PARP抑制剂在卵巢癌中的应用：ASCO指南》解读

        2014年，美国食品和药物监督管理局（FDA）批准了第一个多聚ADP核糖多聚酶（PARP抑制剂）奥拉帕利（Olaparib）治疗已接受≥3线化疗的胚系BRCA突变（gBRCA）卵巢上皮性癌患者；2016年批准卢卡帕利（Rucaparib）用于治疗胚系和体细胞BRCA突变（g/sBRCA）的复发性卵巢癌患者；2017年批准了尼拉帕利（Niraparib）和Olaparib用于对含铂化疗完全或部分缓解患者的维持治疗。 浏览更多请关注本刊微信公众号及当期杂志。     

Antifungal susceptibility testing practices in mycology laboratories in France, 2018

A survey of mycology laboratories for antifungal susceptibility testing (AFST) was undertaken in France in 2018, to better understand the difference in practices between the participating centers and to identify the difficulties they may encounter as well as eventual gaps with published standards and guidelines. The survey captured information from 45 mycology laboratories in France on how they perform AFST (number of strains tested, preferred method, technical and quality aspects, interpretation of the MIC values, reading and interpretation difficulties). Results indicated that 86% of respondents used Etest as AFST method, with a combination of one to seven antifungal agents tested. Most of the participating laboratories used similar technical parameters to perform their AFST method and a large majority used, as recommended, internal and external quality assessments. Almost all the participating mycology laboratories (98%) reported difficulties to interpret the MIC values, especially when no clinical breakpoints are available. The survey highlighted that the current AFST practices in France need homogenization, particularly for MIC reading and interpretation.     

In search of a caring relationship - Nursing students’ notions of interactions in the nurse-patient relationship

The aim of the study is to explore how nursing students talks about their notions on interactions in the relationship between nurse and patient. Empirical data has been obtained from 22 students in their first semester of the Swedish undergraduate nursing programme, applying thematic, individual interviews and using qualitative content analyses. The most significant finding is that although the students had none or limited pre-understanding of caring and the interaction between nurse and patient they were aware of the pre-supposes for establishing such an interplay, articulated in a desire to find out how to build an authentic and trustful caring relationship. Empirical data also show that the students, in the stage of beginners, were trying to identify and relate to basic concepts within caring science. The restricted understanding could then be understood as a matter of the students not being able to express more than they had words for. The results provide new insights into the interactions in the nurse-patient relationship, seen from the perspective of beginner students. These insights could be useful, for lecturers and clinical supervisors, who play a paramount role in the development of each student's acquisition of theoretical and practical knowledge.     

Using probabilistic theory to develop interpretation guidelines for Y-STR profiles

Y-STR profiling makes up a small but important proportion of forensic DNA casework. Often Y-STR profiles are used when autosomal profiling has failed to yield an informative result. Consequently Y-STR profiles are often from the most challenging samples. In addition to these points, Y-STR loci are linked, meaning that evaluation of haplotype probabilities are either based on overly simplified counting methods or computationally costly genetic models, neither of which extend well to the evaluation of mixed Y-STR data. For all of these reasons Y-STR data analysis has not seen the same advances as autosomal STR data. We present here a probabilistic model for the interpretation of Y-STR data. Due to the fact that probabilistic systems for Y-STR data are still some way from reaching active casework, we also describe how data can be analysed in a continuous way to generate interpretational thresholds and guidelines.