bis-(3-diethylamino-propyl)-disulfide

• 分子结构分类: 有机化合物 - 有机氮化合物
• 英文名称: bis-(3-diethylamino-propyl)-disulfide
• 英文别名: 3.3'-Bis-diaethylamino-dipropyldisulfid；Bis-(3-diaethylamino-propyl)-disulfid；3-[3-(diethylamino)propyldisulfanyl]-N,N-diethyl-propan-1-amine；3-[3-(diethylamino)propyldisulfanyl]-N,N-diethylpropan-1-amine
• 化学式: C₁₄H₃₂N₂S₂
• 分子量: 292.553
• InChiKey: AYWGCWLPPWOSIZ-UHFFFAOYSA-N

计算性质

• 辛醇/水分配系数(LogP)：
  3.2
• 重原子数：
  18
• 可旋转键数：
  13
• 环数：
  0.0
• sp3杂化的碳原子比例：
  1.0
• 拓扑面积：
  57.1
• 氢给体数：
  0
• 氢受体数：
  4

反应信息

• 作为反应物：
  描述：

  bis-(3-diethylamino-propyl)-disulfide 、 碘乙烷 在 苯 作用下， 生成 hexa-N-ethyl-N,N'-(4,5-dithia-octanediyl)-di-ammonium; diiodide
  参考文献：
  名称：

  Andrews et al., Journal of the Chemical Society, 1953, p. 2998,3000

  摘要：

  DOI：
• 作为产物：
  描述：

  alkaline earth salt of/the/ methylsulfuric acid 在 乙醇 、 碘 作用下， 生成 bis-(3-diethylamino-propyl)-disulfide
  参考文献：
  名称：

  M EASUREMENT OF P EDIATRIC I LLNESS S EVERITY U SING S IMPLE P RETRANSPORT V ARIABLES

  摘要：

  Objective: To test the hypothesis that pretransport variables can predict in-hospital mortality that will correlate with major interventions and unplanned events during interfacility transport. Methods: A cohort of children (n = 2,253) transported by a specialized pediatric team to a children's hospital were studied. At the time of referral, data collected included age (months), heart rate, systolic blood pressure, respiratory rate, retractions, stridor or wheezing, seizures, skin perfusion, oxygen requirement, and mental status. Using univariate and stepwise logistic regression, variables predictive of in-hospital mortality were selected from a training set (n = 1,111) and assigned integers based on their computed coefficients. Probability of in-hospital mortality was calculated using the total integer score and age. The risk of mortality derived from the training set was validated in the remaining patients (n = 1,142) by comparing the observed and predicted mortalities. Major interventions performed and unplanned events were determined for each of five predetermined mortality risk groups. Results: Variables (integers) predicting in-hospital mortality included systolic blood pressure (11), respiratory rate (6), oxygen requirement (11), and altered mental status (11). Observed mortality was similar to predicted mortality in all risk categories for the validation sample. As risk of mortality increased, so did the performance of major interventions and the occurrence of unplanned events. Conclusion: Four pretransport variables predicted in-hospital mortality. Risk of mortality correlated with the incidence of major patient interventions, and the occurrence of unplanned events increased as well. This model might be useful in comparing different transport systems using severity-adjusted assessment of children requiring interfacility transport.

  DOI：

  10.1080/10903120190939986