目的探索注射用丹参多酚酸对职业性急性化学物中毒性神经系统疾病、轻度中毒性脑病患者的疗效。方法选取2018年6月—2019年6月于黑龙江省第二医院住院的29例职业性急性化学物中毒性神经系统疾病、轻度中毒性脑病患者作为研究对象,将患...目的探索注射用丹参多酚酸对职业性急性化学物中毒性神经系统疾病、轻度中毒性脑病患者的疗效。方法选取2018年6月—2019年6月于黑龙江省第二医院住院的29例职业性急性化学物中毒性神经系统疾病、轻度中毒性脑病患者作为研究对象,将患者随机分为对照组(14例)和观察组(15例)。对照组均给予常规治疗,包括纠正脑缺氧、改善脑血循环、减轻脑水肿、降低颅内压等措施,观察组在常规治疗基础上静滴注射用丹参多酚酸,将注射用丹参多酚酸0.13 g加入到0.9%氯化钠250 m L,1次/d。两组疗程为14 d。比较两组患者治疗前后颅内段血管彩超血流速度及蒙特利尔认知评估量表(MoCA)评分。结果治疗后,两组患者血流速度均显著改善(P<0.05);且观察组颅内动脉流速恢复明显优于对照组(P<0.05)。治疗后,两组MoCA评分均显著升高(P<0.05);且观察组MoCA评分优于对照组(P<0.05)。结论在常规疗法基础上联合注射用丹参多酚酸治疗职业性急性化学物中毒性神经系统疾病、轻度中毒性脑病,可有效改善颅内段血管彩超血流速度,从而改善患者头晕、头痛等不适症状,并有效提高患者认知功能。展开更多
The impact of pesticides on insect pollinators has caused worldwide concern. Both global bee decline and stopping the use of pesticides may have serious consequences for food security. Automated and accurate predictio...The impact of pesticides on insect pollinators has caused worldwide concern. Both global bee decline and stopping the use of pesticides may have serious consequences for food security. Automated and accurate prediction of chemical poisoning of honey bees is a challenging task owing to a lack of understanding of chemical toxicity and introspection. Deep learning(DL) shows potential utility for general and highly variable tasks across fields. Here, we developed a new DL model of deep graph attention convolutional neural networks(GACNN) with the combination of undirected graph(UG) and attention convolutional neural networks(ACNN) to accurately classify chemical poisoning of honey bees. We used a training dataset of 720 pesticides and an external validation dataset of 90 pesticides, which is one order of magnitude larger than the previous datasets. We tested its performance in two ways: poisonous versus nonpoisonous and GACNN versus other frequently-used machine learning models. The first case represents the accuracy in identifying bee poisonous chemicals. The second represents performance advantages. The GACNN achieved ~6% higher performance for predicting toxic samples and more stable with ~7%Matthews Correlation Coefficient(MCC) higher compared to all tested models, demonstrating GACNN is capable of accurately classifying chemicals and has considerable potential in practical applications.In addition, we also summarized and evaluated the mechanisms underlying the response of honey bees to chemical exposure based on the mapping of molecular similarity. Moreover, our cloud platform(http://beetox.cn) of this model provides low-cost universal access to information, which could vitally enhance environmental risk assessment.展开更多
文摘目的探索注射用丹参多酚酸对职业性急性化学物中毒性神经系统疾病、轻度中毒性脑病患者的疗效。方法选取2018年6月—2019年6月于黑龙江省第二医院住院的29例职业性急性化学物中毒性神经系统疾病、轻度中毒性脑病患者作为研究对象,将患者随机分为对照组(14例)和观察组(15例)。对照组均给予常规治疗,包括纠正脑缺氧、改善脑血循环、减轻脑水肿、降低颅内压等措施,观察组在常规治疗基础上静滴注射用丹参多酚酸,将注射用丹参多酚酸0.13 g加入到0.9%氯化钠250 m L,1次/d。两组疗程为14 d。比较两组患者治疗前后颅内段血管彩超血流速度及蒙特利尔认知评估量表(MoCA)评分。结果治疗后,两组患者血流速度均显著改善(P<0.05);且观察组颅内动脉流速恢复明显优于对照组(P<0.05)。治疗后,两组MoCA评分均显著升高(P<0.05);且观察组MoCA评分优于对照组(P<0.05)。结论在常规疗法基础上联合注射用丹参多酚酸治疗职业性急性化学物中毒性神经系统疾病、轻度中毒性脑病,可有效改善颅内段血管彩超血流速度,从而改善患者头晕、头痛等不适症状,并有效提高患者认知功能。
基金This work was supported in part by the National Key Research and Development Program of China(2017YFD0200506)the National Natural Science Foundation of China(21837001 and 21907036).
文摘The impact of pesticides on insect pollinators has caused worldwide concern. Both global bee decline and stopping the use of pesticides may have serious consequences for food security. Automated and accurate prediction of chemical poisoning of honey bees is a challenging task owing to a lack of understanding of chemical toxicity and introspection. Deep learning(DL) shows potential utility for general and highly variable tasks across fields. Here, we developed a new DL model of deep graph attention convolutional neural networks(GACNN) with the combination of undirected graph(UG) and attention convolutional neural networks(ACNN) to accurately classify chemical poisoning of honey bees. We used a training dataset of 720 pesticides and an external validation dataset of 90 pesticides, which is one order of magnitude larger than the previous datasets. We tested its performance in two ways: poisonous versus nonpoisonous and GACNN versus other frequently-used machine learning models. The first case represents the accuracy in identifying bee poisonous chemicals. The second represents performance advantages. The GACNN achieved ~6% higher performance for predicting toxic samples and more stable with ~7%Matthews Correlation Coefficient(MCC) higher compared to all tested models, demonstrating GACNN is capable of accurately classifying chemicals and has considerable potential in practical applications.In addition, we also summarized and evaluated the mechanisms underlying the response of honey bees to chemical exposure based on the mapping of molecular similarity. Moreover, our cloud platform(http://beetox.cn) of this model provides low-cost universal access to information, which could vitally enhance environmental risk assessment.
