BACKGROUND Most species of aconite contain highly toxic aconitines,the oral ingestion of which can be fatal,primarily because they cause ventricular arrhythmias.We describe a case of severe aconite poisoning that was ...BACKGROUND Most species of aconite contain highly toxic aconitines,the oral ingestion of which can be fatal,primarily because they cause ventricular arrhythmias.We describe a case of severe aconite poisoning that was successfully treated through venoarterial extracorporeal membrane oxygenation(VA-ECMO)and in which detailed toxicological analyses of the aconite roots and biological samples were performed using liquid chromatography-tandem mass spectrometry(LC-MS/MS).CASE SUMMARY A 23-year-old male presented to the emergency room with circulatory collapse and ventricular arrhythmia after ingesting approximately half of a root labeled,“Aconitum japonicum Thunb”.Two hours after arrival,VA-ECMO was initiated as circulatory collapse became refractory to antiarrhythmics and vasopressors.Nine hours after arrival,an electrocardiogram revealed a return to sinus rhythm.The patient was weaned off VA-ECMO and the ventilator on hospital days 3 and 5,respectively.On hospital day 15,he was transferred to a psychiatric hospital.The other half of the root and his biological samples were toxicologically analyzed using LC-MS/MS,revealing 244.3 mg/kg of aconitine and 24.7 mg/kg of mesaconitine in the root.Serum on admission contained 1.50 ng/mL of aconitine.Beyond hospital day 2,neither were detected.Urine on admission showed 149.09 ng/mL of aconitine and 3.59 ng/mL of mesaconitine,but these rapidly decreased after hospital day 3.CONCLUSION The key to saving the life of a patient with severe aconite poisoning is to introduce VA-ECMO as soon as possible.展开更多
This article compares the size of selected subsets using nonparametric subset selection rules with two different scoring rules for the observations. The scoring rules are based on the expected values of order statisti...This article compares the size of selected subsets using nonparametric subset selection rules with two different scoring rules for the observations. The scoring rules are based on the expected values of order statistics of the uniform distribution (yielding rank values) and of the normal distribution (yielding normal score values). The comparison is made using state motor vehicle traffic fatality rates, published in a 2016 article, with fifty-one states (including DC as a state) and over a nineteen-year period (1994 through 2012). The earlier study considered four block design selection rules—two for choosing a subset to contain the “best” population (i.e., state with lowest mean fatality rate) and two for the “worst” population (i.e., highest mean rate) with a probability of correct selection chosen to be 0.90. Two selection rules based on normal scores resulted in selected subset sizes substantially smaller than corresponding rules based on ranks (7 vs. 16 and 3 vs. 12). For two other selection rules, the subsets chosen were very close in size (within one). A comparison is also made using state homicide rates, published in a 2022 article, with fifty states and covering eight years. The results are qualitatively the same as those obtained with the motor vehicle traffic fatality rates.展开更多
Introduction: Data on mortality in acute kidney injury (AKI) derives from high-income countries where AKI is hospital-acquired and occurs in elderly patients with a high burden of cardiovascular disease. In sub-Sahara...Introduction: Data on mortality in acute kidney injury (AKI) derives from high-income countries where AKI is hospital-acquired and occurs in elderly patients with a high burden of cardiovascular disease. In sub-Saharan Africa (SSA), AKI is community-acquired occurring in healthy young adults. We aimed to identify predictors of fatal outcomes in patients with AKI in two tertiary hospitals in Cameroon. Methods: Medical records of adults with confirmed AKI, from January 2018 to March 2020 were retrieved. The outcomes of interest were in-hospital deaths and presumed causes of death. We used multiple logistic regressions modeling to identify predictors of death. The study was approved by the ethics boards of both hospitals. Values were considered significant for a p-value of 0.05. Results: We included 285 patient records (37.2% females). The mean (SD) age was 50.1 (19.0) years. Hypertension (n = 97, 34.0%), organ failure (n = 88, 30.9%), and diabetes (n = 60, 21.1%) were the main comorbidities. The majority of patients had community-acquired AKI (78.6%, n = 224), were KDIGO stage 3 (88.8%, n = 253), and needed dialysis (52.6%, n = 150). Up to 16.7% (n = 25) did not receive what was needed. The in-hospital mortality rate was 29.1% (n = 83). Lack of access to dialysis (OR = 27.8;CI: 5.2 - 149.3, p = 0.001), hypotension (OR = 11.8;CI: 1.3 - 24.8;p = 0.001) and ICU admission (OR = 5.7;CI: 1.3 - 24.8, p = 0.001) were predictors of mortality. The presence of co-morbidities or underlying diseases (n = 46, 55%) were the main causes of death. Conclusions: In-hospital AKI mortality is high, as in other low- and middle-income economies. Lack of access to dialysis and the severity of the underlying illness are major predictors of death.展开更多
BACKGROUND The spread of the severe acute respiratory syndrome coronavirus 2 outbreak worldwide has caused concern regarding the mortality rate caused by the infection.The determinants of mortality on a global scale c...BACKGROUND The spread of the severe acute respiratory syndrome coronavirus 2 outbreak worldwide has caused concern regarding the mortality rate caused by the infection.The determinants of mortality on a global scale cannot be fully understood due to lack of information.AIM To identify key factors that may explain the variability in case lethality across countries.METHODS We identified 21 Potential risk factors for coronavirus disease 2019(COVID-19)case fatality rate for all the countries with available data.We examined univariate relationships of each variable with case fatality rate(CFR),and all independent variables to identify candidate variables for our final multiple model.Multiple regression analysis technique was used to assess the strength of relationship.RESULTS The mean of COVID-19 mortality was 1.52±1.72%.There was a statistically significant inverse correlation between health expenditure,and number of computed tomography scanners per 1 million with CFR,and significant direct correlation was found between literacy,and air pollution with CFR.This final model can predict approximately 97%of the changes in CFR.CONCLUSION The current study recommends some new predictors explaining affect mortality rate.Thus,it could help decision-makers develop health policies to fight COVID-19.展开更多
Traffic intersections are incredibly dangerous for drivers and pedestrians. Statistics from both Canada and the U.S. show a high number of fatalities and serious injuries related to crashes at intersections. In Canada...Traffic intersections are incredibly dangerous for drivers and pedestrians. Statistics from both Canada and the U.S. show a high number of fatalities and serious injuries related to crashes at intersections. In Canada, during 2019, the National Collision Database shows that 28% of traffic fatalities and 42% of serious injuries occurred at intersections. Likewise, the U.S. National Highway Traffic Administration (NHTSA) found that about 40% of the estimated 5,811,000 accidents in the U.S. during the year studied were intersection-related crashes. In fact, a major survey by the car insurance industry found that nearly 85% of drivers could not identify the correct action to take when approaching a yellow traffic light at an intersection. One major reason for these accidents is the “yellow light dilemma,” the ambiguous situation where a driver should stop or proceed forward when unexpectedly faced with a yellow light. This situation is even further exacerbated by the tendency of aggressive drivers to inappropriately speed up on the yellow just to get through the traffic light. A survey of Canadian drivers conducted by the Traffic Injury Research Foundation found that 9% of drivers admitted to speeding up to get through a traffic light. Another reason for these accidents is the increased danger of making a left-hand turn on yellow. According to the National Highway Traffic Safety Association (NHTSA), left turns occur in approximately 22.2% of collisions—as opposed to just 1.2% for right turns. Moreover, a study by CNN found left turns are three times as likely to kill pedestrians than right turns. The reason left turns are so much more likely to cause an accident is because they take a driver against traffic and in the path of oncoming cars. Additionally, most of these left turns occur at the driver’s discretion—as opposed to the distressingly brief left-hand arrow at busy intersections. Drive Safe Now proposes a workable solution for reducing the number of accidents occurring during a yellow light at intersections. We believe this fairly simple solution will save lives, prevent injuries, reduce damage to public and private property, and decrease insurance costs.展开更多
This paper examines the relationship between fatal road traffic accidents and potential predictors using multilayer perceptron artificial neural network (MLANN) models. The initial analysis employed twelve potential p...This paper examines the relationship between fatal road traffic accidents and potential predictors using multilayer perceptron artificial neural network (MLANN) models. The initial analysis employed twelve potential predictors, including traffic volume, prevailing weather conditions, roadway characteristics and features, drivers’ age and gender, and number of lanes. Based on the output of the model and the variables’ importance factors, seven significant variables are identified and used for further analysis to improve the performance of models. The model is optimized by systematically changing the parameters, including the number of hidden layers and the activation function of both the hidden and output layers. The performances of the MLANN models are evaluated using the percentage of the achieved accuracy, R-squared, and Sum of Square Error (SSE) functions.展开更多
Although there has been a slight decrease in road traffic crashes, fatalities, and injuries in recent years, HCMC (Ho Chi Minh City) will continue to encounter challenges in mitigating and preventing road crashes. Thi...Although there has been a slight decrease in road traffic crashes, fatalities, and injuries in recent years, HCMC (Ho Chi Minh City) will continue to encounter challenges in mitigating and preventing road crashes. This study analyzes road crash data from the past five years, obtained from the Road-Railway Police Bureau (PC08) and TSB (Traffic Safety Board) in HCMC. This analysis gives us valuable insights into road crash patterns, characteristics, and underlying causes. This comprehensive understanding serves as a scientific foundation for developing cohesive strategies and implementing targeted solutions to address road traffic safety issues more effectively in the future.展开更多
文摘BACKGROUND Most species of aconite contain highly toxic aconitines,the oral ingestion of which can be fatal,primarily because they cause ventricular arrhythmias.We describe a case of severe aconite poisoning that was successfully treated through venoarterial extracorporeal membrane oxygenation(VA-ECMO)and in which detailed toxicological analyses of the aconite roots and biological samples were performed using liquid chromatography-tandem mass spectrometry(LC-MS/MS).CASE SUMMARY A 23-year-old male presented to the emergency room with circulatory collapse and ventricular arrhythmia after ingesting approximately half of a root labeled,“Aconitum japonicum Thunb”.Two hours after arrival,VA-ECMO was initiated as circulatory collapse became refractory to antiarrhythmics and vasopressors.Nine hours after arrival,an electrocardiogram revealed a return to sinus rhythm.The patient was weaned off VA-ECMO and the ventilator on hospital days 3 and 5,respectively.On hospital day 15,he was transferred to a psychiatric hospital.The other half of the root and his biological samples were toxicologically analyzed using LC-MS/MS,revealing 244.3 mg/kg of aconitine and 24.7 mg/kg of mesaconitine in the root.Serum on admission contained 1.50 ng/mL of aconitine.Beyond hospital day 2,neither were detected.Urine on admission showed 149.09 ng/mL of aconitine and 3.59 ng/mL of mesaconitine,but these rapidly decreased after hospital day 3.CONCLUSION The key to saving the life of a patient with severe aconite poisoning is to introduce VA-ECMO as soon as possible.
文摘This article compares the size of selected subsets using nonparametric subset selection rules with two different scoring rules for the observations. The scoring rules are based on the expected values of order statistics of the uniform distribution (yielding rank values) and of the normal distribution (yielding normal score values). The comparison is made using state motor vehicle traffic fatality rates, published in a 2016 article, with fifty-one states (including DC as a state) and over a nineteen-year period (1994 through 2012). The earlier study considered four block design selection rules—two for choosing a subset to contain the “best” population (i.e., state with lowest mean fatality rate) and two for the “worst” population (i.e., highest mean rate) with a probability of correct selection chosen to be 0.90. Two selection rules based on normal scores resulted in selected subset sizes substantially smaller than corresponding rules based on ranks (7 vs. 16 and 3 vs. 12). For two other selection rules, the subsets chosen were very close in size (within one). A comparison is also made using state homicide rates, published in a 2022 article, with fifty states and covering eight years. The results are qualitatively the same as those obtained with the motor vehicle traffic fatality rates.
文摘Introduction: Data on mortality in acute kidney injury (AKI) derives from high-income countries where AKI is hospital-acquired and occurs in elderly patients with a high burden of cardiovascular disease. In sub-Saharan Africa (SSA), AKI is community-acquired occurring in healthy young adults. We aimed to identify predictors of fatal outcomes in patients with AKI in two tertiary hospitals in Cameroon. Methods: Medical records of adults with confirmed AKI, from January 2018 to March 2020 were retrieved. The outcomes of interest were in-hospital deaths and presumed causes of death. We used multiple logistic regressions modeling to identify predictors of death. The study was approved by the ethics boards of both hospitals. Values were considered significant for a p-value of 0.05. Results: We included 285 patient records (37.2% females). The mean (SD) age was 50.1 (19.0) years. Hypertension (n = 97, 34.0%), organ failure (n = 88, 30.9%), and diabetes (n = 60, 21.1%) were the main comorbidities. The majority of patients had community-acquired AKI (78.6%, n = 224), were KDIGO stage 3 (88.8%, n = 253), and needed dialysis (52.6%, n = 150). Up to 16.7% (n = 25) did not receive what was needed. The in-hospital mortality rate was 29.1% (n = 83). Lack of access to dialysis (OR = 27.8;CI: 5.2 - 149.3, p = 0.001), hypotension (OR = 11.8;CI: 1.3 - 24.8;p = 0.001) and ICU admission (OR = 5.7;CI: 1.3 - 24.8, p = 0.001) were predictors of mortality. The presence of co-morbidities or underlying diseases (n = 46, 55%) were the main causes of death. Conclusions: In-hospital AKI mortality is high, as in other low- and middle-income economies. Lack of access to dialysis and the severity of the underlying illness are major predictors of death.
文摘BACKGROUND The spread of the severe acute respiratory syndrome coronavirus 2 outbreak worldwide has caused concern regarding the mortality rate caused by the infection.The determinants of mortality on a global scale cannot be fully understood due to lack of information.AIM To identify key factors that may explain the variability in case lethality across countries.METHODS We identified 21 Potential risk factors for coronavirus disease 2019(COVID-19)case fatality rate for all the countries with available data.We examined univariate relationships of each variable with case fatality rate(CFR),and all independent variables to identify candidate variables for our final multiple model.Multiple regression analysis technique was used to assess the strength of relationship.RESULTS The mean of COVID-19 mortality was 1.52±1.72%.There was a statistically significant inverse correlation between health expenditure,and number of computed tomography scanners per 1 million with CFR,and significant direct correlation was found between literacy,and air pollution with CFR.This final model can predict approximately 97%of the changes in CFR.CONCLUSION The current study recommends some new predictors explaining affect mortality rate.Thus,it could help decision-makers develop health policies to fight COVID-19.
文摘Traffic intersections are incredibly dangerous for drivers and pedestrians. Statistics from both Canada and the U.S. show a high number of fatalities and serious injuries related to crashes at intersections. In Canada, during 2019, the National Collision Database shows that 28% of traffic fatalities and 42% of serious injuries occurred at intersections. Likewise, the U.S. National Highway Traffic Administration (NHTSA) found that about 40% of the estimated 5,811,000 accidents in the U.S. during the year studied were intersection-related crashes. In fact, a major survey by the car insurance industry found that nearly 85% of drivers could not identify the correct action to take when approaching a yellow traffic light at an intersection. One major reason for these accidents is the “yellow light dilemma,” the ambiguous situation where a driver should stop or proceed forward when unexpectedly faced with a yellow light. This situation is even further exacerbated by the tendency of aggressive drivers to inappropriately speed up on the yellow just to get through the traffic light. A survey of Canadian drivers conducted by the Traffic Injury Research Foundation found that 9% of drivers admitted to speeding up to get through a traffic light. Another reason for these accidents is the increased danger of making a left-hand turn on yellow. According to the National Highway Traffic Safety Association (NHTSA), left turns occur in approximately 22.2% of collisions—as opposed to just 1.2% for right turns. Moreover, a study by CNN found left turns are three times as likely to kill pedestrians than right turns. The reason left turns are so much more likely to cause an accident is because they take a driver against traffic and in the path of oncoming cars. Additionally, most of these left turns occur at the driver’s discretion—as opposed to the distressingly brief left-hand arrow at busy intersections. Drive Safe Now proposes a workable solution for reducing the number of accidents occurring during a yellow light at intersections. We believe this fairly simple solution will save lives, prevent injuries, reduce damage to public and private property, and decrease insurance costs.
文摘This paper examines the relationship between fatal road traffic accidents and potential predictors using multilayer perceptron artificial neural network (MLANN) models. The initial analysis employed twelve potential predictors, including traffic volume, prevailing weather conditions, roadway characteristics and features, drivers’ age and gender, and number of lanes. Based on the output of the model and the variables’ importance factors, seven significant variables are identified and used for further analysis to improve the performance of models. The model is optimized by systematically changing the parameters, including the number of hidden layers and the activation function of both the hidden and output layers. The performances of the MLANN models are evaluated using the percentage of the achieved accuracy, R-squared, and Sum of Square Error (SSE) functions.
文摘Although there has been a slight decrease in road traffic crashes, fatalities, and injuries in recent years, HCMC (Ho Chi Minh City) will continue to encounter challenges in mitigating and preventing road crashes. This study analyzes road crash data from the past five years, obtained from the Road-Railway Police Bureau (PC08) and TSB (Traffic Safety Board) in HCMC. This analysis gives us valuable insights into road crash patterns, characteristics, and underlying causes. This comprehensive understanding serves as a scientific foundation for developing cohesive strategies and implementing targeted solutions to address road traffic safety issues more effectively in the future.
