BACKGROUND Extreme heat exposure is a growing health problem,and the effects of heat on the gastrointestinal(GI)tract is unknown.This study aimed to assess the incidence of GI symptoms associated with heatstroke and i...BACKGROUND Extreme heat exposure is a growing health problem,and the effects of heat on the gastrointestinal(GI)tract is unknown.This study aimed to assess the incidence of GI symptoms associated with heatstroke and its impact on outcomes.AIM To assess the incidence of GI symptoms associated with heatstroke and its impact on outcomes.METHODS Patients admitted to the intensive care unit(ICU)due to heatstroke were included from 83 centres.Patient history,laboratory results,and clinically relevant outcomes were recorded at ICU admission and daily until up to day 15,ICU discharge,or death.GI symptoms,including nausea/vomiting,diarrhoea,flatulence,and bloody stools,were recorded.The characteristics of patients with heatstroke concomitant with GI symptoms were described.Multivariable regression analyses were performed to determine significant predictors of GI symptoms.RESULTS A total of 713 patients were included in the final analysis,of whom 132(18.5%)patients had at least one GI symptom during their ICU stay,while 26(3.6%)suffered from more than one symptom.Patients with GI symptoms had a significantly higher ICU stay compared with those without.The mortality of patients who had two or more GI symptoms simultaneously was significantly higher than that in those with one GI symptom.Multivariable logistic regression analysis revealed that older patients with a lower GCS score on admission were more likely to experience GI symptoms.CONCLUSION The GI manifestations of heatstroke are common and appear to impact clinically relevant hospitalization outcomes.展开更多
Accurately predicting downhole risk before drilling in new exploration areas is one of the difficulties.Using intelligent algorithms to explore the complex relationship between multi-source data and downhole risk is a...Accurately predicting downhole risk before drilling in new exploration areas is one of the difficulties.Using intelligent algorithms to explore the complex relationship between multi-source data and downhole risk is a hot research topic and frontier in this field.However,due to the small number and uneven distribution of drilled wells in new exploration areas and the lack of sample data related to risk,the training model has insufficient generalization ability,and thus the prediction is not effective.In this paper,a drilling risk profile(depth domain)rich in geological and engineering information is constructed by introducing a quantitative evaluation method for drilling risk of drilled wells,which can provide sufficient risk sample data for model training and thus solve the small sample problem.For the problem of uneven distribution of drilling wells in new exploration areas,the concept of virtual wells and their deployment methods were proposed.Besides,two methods for calculating rock mechanical parameters of virtual wells were proposed,and the accuracy and applicability of the two methods are analyzed.The LSTM deep learning model was optimized to tap the quantitative relationship between drilling risk profiles and multi-source data(e.g.,seismic,logging,and rock mechanical parameters).The model was validated to have an average relative error of 9.19%.The quantitative prediction of the drilling risk profile of the virtual well was achieved using the trained LSTM model and the calculation of the relevant parameters of the virtual well.Finally,based on the sequential Gaussian simulation method and the risk distribution of drilled and virtual wells,a regional 3D drilling risk model was constructed.The analysis of real cases shows that the addition of virtual wells can significantly improve the identification of regional drilling risks and the prediction accuracy of pre-drill drilling risks in unexplored areas can be improved by up to 21%compared with the 3D risk model constructed based on drilled wells only.展开更多
基金Supported by National Key R&D Program of China,No.2022YFC25045001.3.5 Project for Disciplines of Excellence,West China Hospital,Sichuan University,No.ZYGD23012.
文摘BACKGROUND Extreme heat exposure is a growing health problem,and the effects of heat on the gastrointestinal(GI)tract is unknown.This study aimed to assess the incidence of GI symptoms associated with heatstroke and its impact on outcomes.AIM To assess the incidence of GI symptoms associated with heatstroke and its impact on outcomes.METHODS Patients admitted to the intensive care unit(ICU)due to heatstroke were included from 83 centres.Patient history,laboratory results,and clinically relevant outcomes were recorded at ICU admission and daily until up to day 15,ICU discharge,or death.GI symptoms,including nausea/vomiting,diarrhoea,flatulence,and bloody stools,were recorded.The characteristics of patients with heatstroke concomitant with GI symptoms were described.Multivariable regression analyses were performed to determine significant predictors of GI symptoms.RESULTS A total of 713 patients were included in the final analysis,of whom 132(18.5%)patients had at least one GI symptom during their ICU stay,while 26(3.6%)suffered from more than one symptom.Patients with GI symptoms had a significantly higher ICU stay compared with those without.The mortality of patients who had two or more GI symptoms simultaneously was significantly higher than that in those with one GI symptom.Multivariable logistic regression analysis revealed that older patients with a lower GCS score on admission were more likely to experience GI symptoms.CONCLUSION The GI manifestations of heatstroke are common and appear to impact clinically relevant hospitalization outcomes.
基金General Program of National Natural Science Foundation of China(52274024,52074326)。
文摘Accurately predicting downhole risk before drilling in new exploration areas is one of the difficulties.Using intelligent algorithms to explore the complex relationship between multi-source data and downhole risk is a hot research topic and frontier in this field.However,due to the small number and uneven distribution of drilled wells in new exploration areas and the lack of sample data related to risk,the training model has insufficient generalization ability,and thus the prediction is not effective.In this paper,a drilling risk profile(depth domain)rich in geological and engineering information is constructed by introducing a quantitative evaluation method for drilling risk of drilled wells,which can provide sufficient risk sample data for model training and thus solve the small sample problem.For the problem of uneven distribution of drilling wells in new exploration areas,the concept of virtual wells and their deployment methods were proposed.Besides,two methods for calculating rock mechanical parameters of virtual wells were proposed,and the accuracy and applicability of the two methods are analyzed.The LSTM deep learning model was optimized to tap the quantitative relationship between drilling risk profiles and multi-source data(e.g.,seismic,logging,and rock mechanical parameters).The model was validated to have an average relative error of 9.19%.The quantitative prediction of the drilling risk profile of the virtual well was achieved using the trained LSTM model and the calculation of the relevant parameters of the virtual well.Finally,based on the sequential Gaussian simulation method and the risk distribution of drilled and virtual wells,a regional 3D drilling risk model was constructed.The analysis of real cases shows that the addition of virtual wells can significantly improve the identification of regional drilling risks and the prediction accuracy of pre-drill drilling risks in unexplored areas can be improved by up to 21%compared with the 3D risk model constructed based on drilled wells only.
基金financial support from the National Natural Science Foundation of China(Nos.51371030 and 51571020)the National Key Research and Development Program of China(No.2016YFB0700505)the National High Technology Research and Development Program of China(No.2015AA034201)
