五种抗真菌药物对皮肤真菌的体外敏感性对比试验研究

应用五种抗真菌药物对引起人、兽皮癣病的石膏样毛癣菌、堇色毛癣菌的体外敏感性试验进行对比观察,旨在指导临床治疗用药。方法采用沙保弱氏药液培养基种菌试管法。结果五种药物对石膏样毛癣菌的MIC、MFC值分别是：足光粉0．78—1．56mg／ml,达克宁霜3．2－6．4mg／ml,皮康王4－8mg／ml,脚癣一次净7．8－15．6μl／ml,脚癣狐臭宁31.2－62．4μl／ml;对堇色毛癣菌的MIC、MFC值分别是：足光粉1．56－3．12mg／ml,达克宁霜1．6－3．2mg／ml,皮康王8－16mg／ml,脚癣一次净1.56－31．2μl／ml,脚癣狐臭宁31．2－62．4μl／ml。结论足光粉对石膏样毛癣菌的MIC、MFC值最小,脚癣狐臭宁对石膏样毛癣菌、堇色毛癣菌的MIC、MFC值相同。提示足光粉在临床上治疗单独一种真菌感染的皮癣病效佳,脚癣狐臭宁在临床上治疗两种以上真菌混合感染的皮癣病效果最优。     

8种常用抗菌渔药对嗜水气单胞菌的抗菌效果比较

采用二倍稀释法及抑菌圈法测定了目前常用的8种抗菌渔药对嗜水气单胞菌的最低抑菌浓度(MIC),比较了这8种抗菌渔药单独用药与联合用药对嗜水气单胞菌的抗菌效果.结果表明,8种抗菌渔药对嗜水气单胞菌的MIC分别为:甲砜霉素1.6μg/mL,庆大霉素、氟甲砜霉素、烟酸诺氟沙星和链霉素均为3.2μg/mL,氟哌酸和土霉素均为12.8μg/mL,而新诺明在12.8μg/mL时仍无抗菌效果.8种抗菌渔药在5倍MIC下,抑菌圈直径分别为:链霉素2.60 cm,氟哌酸2.10 cm,土霉素1.97 cm,烟酸诺氟沙星1.73 cm,氟甲砜霉素1.38 cm,甲砜霉素1.23 cm,庆大霉素1.18 cm;联合用药的抑菌圈直径分别为:链霉素与土霉素为2.63cm,氟甲砜霉素与氟哌酸为2.58 cm,氟甲砜霉素与链霉素为2.54 cm,链霉素与庆大霉素为2.50 cm,氟甲砜霉素与土霉素为2.30 cm,土霉素与氟哌酸为2.14 cm,氟甲砜霉素与烟酸诺氟沙星为1.74 cm,烟酸诺氟沙星与甲砜霉素为1.62 cm.对抗菌渔药最佳参数及临床用药量与药物负荷量的关系探讨表明,抗嗜水气单胞菌用药应首选链霉素、土霉素和烟酸诺氟沙星,其次选甲砜霉素和庆大霉素;联合用药与单独用药的效果无差异.     

Textual data transformations using natural language processing for risk assessment

Underlying information about failure, including observations made in free text, can be a good source for understanding, analyzing, and extracting meaningful information for determining causation. The unstructured nature of natural language expression demands advanced methodology to identify its underlying features. There is no available solution to utilize unstructured data for risk assessment purposes. Due to the scarcity of relevant data, textual data can be a vital learning source for developing a risk assessment methodology. This work addresses the knowledge gap in extracting relevant features from textual data to develop cause–effect scenarios with minimal manual interpretation. This study applies natural language processing and text-mining techniques to extract features from past accident reports. The extracted features are transformed into parametric form with the help of fuzzy set theory and utilized in Bayesian networks as prior probabilities for risk assessment. An application of the proposed methodology is shown in microbiologically influenced corrosion-related incident reports available from the Pipeline and Hazardous Material Safety Administration database. In addition, the trained named entity recognition (NER) model is verified on eight incidents, showing a promising preliminary result for identifying all relevant features from textual data and demonstrating the robustness and applicability of the NER method. The proposed methodology can be used in domain-specific risk assessment to analyze, predict, and prevent future mishaps, ameliorating overall process safety.