Hemodynamic monitoring and optimization improve postoperative outcome during high-risk surgery.However,hemodynamic management practices among Chinese anesthesiologists are largely unknown.This study sought to evaluate...Hemodynamic monitoring and optimization improve postoperative outcome during high-risk surgery.However,hemodynamic management practices among Chinese anesthesiologists are largely unknown.This study sought to evaluate the current intraoperative hemodynamic management practices for high-risk surgery patients in China.From September 2010 to November 2011,we surveyed anesthesiologists working in the operating rooms of 265 hospitals representing 28 Chinese provinces.All questionnaires were distributed to department chairs of anesthesiology or practicing anesthesiologists.Once completed,the 29-item questionnaires were collected and analyzed.Two hundred and 10 questionnaires from 265 hospitals in China were collected.We found that 91.4%of anesthesiologists monitored invasive arterial pressure,82.9%monitored central venous pressure（CVP）,13.3%monitored cardiac output（CO）,10.5%monitored mixed venous saturation,and less than 2%monitored pulse pressure variation（PPV） or systolic pressure variation（SPV） during high-risk surgery.The majority（88%） of anesthesiologists relied on clinical experience as an indicator for volume expansion and more than 80%relied on blood pressure,CVP and urine output.Anesthesiologists in China do not own enough attention on hemodynamic parameters such as PPV,SPV and CO during fluid management in high-risk surgical patients.The lack of CO monitoring may be attributed largely to the limited access to technologies,the cost of the devices and the lack of education on how to use them.There is a need for improving access to these technologies as well as an opportunity to create guidelines and education for hemodynamic optimization in China.展开更多
Computational modeling has emerged as a time-saving and cost-effective alternative to traditional animal testing for assessing chemicals for their potential hazards.However,few computational modeling studies for immun...Computational modeling has emerged as a time-saving and cost-effective alternative to traditional animal testing for assessing chemicals for their potential hazards.However,few computational modeling studies for immunotoxicity were reported,with few models available for predicting toxicants due to the lack of training data and the complex mechanisms of immunotoxicity.In this study,we employed a data-driven quantitative structure–activity relationship(QSAR)modeling workflow to extensively enlarge the limited training data by revealing multiple targets involved in immunotoxicity.To this end,a probe data set of 6,341 chemicals was obtained from a high-throughput screening(HTS)assay testing for the activation of the aryl hydrocarbon receptor(AhR)signaling pathway,a key event leading to immunotoxicity.Searching this probe data set against PubChem yielded 3,183 assays with testing results for varying proportions of these 6,341 compounds.100 assays were selected to develop QSAR models based on their correlations to AhR agonism.Twelve individual QSAR models were built for each assay using combinations of four machine-learning algorithms and three molecular fingerprints.5-fold cross-validation of the resulting models showed good predictivity(average CCR=0.73).A total of 20 assays were further selected based on QSAR model performance,and their resulting QSAR models showed good predictivity of potential immunotoxicants from external chemicals.This study provides a computational modeling strategy that can utilize large public toxicity data sets for modeling immunotoxicity and other toxicity endpoints,which have limited training data and complicated toxicity mechanisms.展开更多
文摘Hemodynamic monitoring and optimization improve postoperative outcome during high-risk surgery.However,hemodynamic management practices among Chinese anesthesiologists are largely unknown.This study sought to evaluate the current intraoperative hemodynamic management practices for high-risk surgery patients in China.From September 2010 to November 2011,we surveyed anesthesiologists working in the operating rooms of 265 hospitals representing 28 Chinese provinces.All questionnaires were distributed to department chairs of anesthesiology or practicing anesthesiologists.Once completed,the 29-item questionnaires were collected and analyzed.Two hundred and 10 questionnaires from 265 hospitals in China were collected.We found that 91.4%of anesthesiologists monitored invasive arterial pressure,82.9%monitored central venous pressure（CVP）,13.3%monitored cardiac output（CO）,10.5%monitored mixed venous saturation,and less than 2%monitored pulse pressure variation（PPV） or systolic pressure variation（SPV） during high-risk surgery.The majority（88%） of anesthesiologists relied on clinical experience as an indicator for volume expansion and more than 80%relied on blood pressure,CVP and urine output.Anesthesiologists in China do not own enough attention on hemodynamic parameters such as PPV,SPV and CO during fluid management in high-risk surgical patients.The lack of CO monitoring may be attributed largely to the limited access to technologies,the cost of the devices and the lack of education on how to use them.There is a need for improving access to these technologies as well as an opportunity to create guidelines and education for hemodynamic optimization in China.
基金National Institute of General Medical Sciences(Grant R01GM148743)National Institute of Child Health and Human Development(Grant UHD113039)+1 种基金National Science Foundation(Grant 2402311)National Institute of Environmental Health Sciences(Grants R01ES031080 and R35ES031709).
文摘Computational modeling has emerged as a time-saving and cost-effective alternative to traditional animal testing for assessing chemicals for their potential hazards.However,few computational modeling studies for immunotoxicity were reported,with few models available for predicting toxicants due to the lack of training data and the complex mechanisms of immunotoxicity.In this study,we employed a data-driven quantitative structure–activity relationship(QSAR)modeling workflow to extensively enlarge the limited training data by revealing multiple targets involved in immunotoxicity.To this end,a probe data set of 6,341 chemicals was obtained from a high-throughput screening(HTS)assay testing for the activation of the aryl hydrocarbon receptor(AhR)signaling pathway,a key event leading to immunotoxicity.Searching this probe data set against PubChem yielded 3,183 assays with testing results for varying proportions of these 6,341 compounds.100 assays were selected to develop QSAR models based on their correlations to AhR agonism.Twelve individual QSAR models were built for each assay using combinations of four machine-learning algorithms and three molecular fingerprints.5-fold cross-validation of the resulting models showed good predictivity(average CCR=0.73).A total of 20 assays were further selected based on QSAR model performance,and their resulting QSAR models showed good predictivity of potential immunotoxicants from external chemicals.This study provides a computational modeling strategy that can utilize large public toxicity data sets for modeling immunotoxicity and other toxicity endpoints,which have limited training data and complicated toxicity mechanisms.
