A primary aortoenteric fistula (PAEF), defined as a communication between the native aorta and the gastrointestinal tract, is a rare cause of gastrointes-tinal bleeding. The preoperative diagnosis of PAEF is extremely...A primary aortoenteric fistula (PAEF), defined as a communication between the native aorta and the gastrointestinal tract, is a rare cause of gastrointes-tinal bleeding. The preoperative diagnosis of PAEF is extremely difficult. Consequently, PAEF may cause sudden and unexpected death. We present an autopsy case of a 68-year-old man who died of massive gastro-intestinal bleeding due to a PAEF. Autopsy revealed a pinhole rupture located on the third part of the duode-nal mucosa and fistulized into the adjacent abdominal aortic aneurysm (AAA). Our case indicates that the aortoenteric fistula can result in fatal gastrointestinal bleeding. Consequently, a PAEF should be included in the differential diagnosis of gastrointestinal bleeding.展开更多
Lycorine and galantamine are natural alkaloids found in Amaryllidaceae plants, such as narcissus. Narcissus leaves and roots are sometimes accidentally ingested because they resemble vegetables. Lycorine and galantami...Lycorine and galantamine are natural alkaloids found in Amaryllidaceae plants, such as narcissus. Narcissus leaves and roots are sometimes accidentally ingested because they resemble vegetables. Lycorine and galantamine are toxic and cause such effects as nausea, vomiting, and abdominal pain, when accidentally ingested. In a case of narcissus poisoning, the detection of lycorine and galantamine in biological samples is vital to determine whether they have been ingested. This study establishes a liquid chromatography-tandem mass spectrometry (LC/MS/MS) method to measure the lycorine and galantamine content of human serum, which can be used for mild to fatal poisoning cases. A serum pretreatment procedure was performed using acetonitrile and QuEChERS AOAC powder. The separation of the compounds was conducted using a pentafluorophenyl column, CAPCELL CORE PFP (2.1 mm I.D. × 100 mm, 2.7 μm). Lycorine, galantamine, and galantamine-d<sub>6</sub> (internal standard) were identified by the transitions of m/z 288 → 147, m/z 288 → 213, and m/z 294 → 216, respectively. The calibration curves were linear in the ranges of 0.05 to 5 ng/mL and 5 to 100 ng/mL, with R<sup>2</sup> > 0.999. The precision and accuracy were within the permissible range. The matrix effects of lycorine and galantamine were 94.3% - 98.4% and 87.8% - 91.1%, respectively. The extraction recovery rates of lycorine and galantamine were 101.9% - 112.7% and 95.6% - 107.1%, respectively. The present method detected lycorine and galantamine in the sera of three patients with mild poisoning that had accidentally ingested. This method is applicable in cases of lycorine and galantamine poisoning.展开更多
Background:Diabetes and hypertension are two of the commonest diseases in the world.As they unfavorably affect people of different age groups,they have become a cause of concern and must be predicted and diagnosed wel...Background:Diabetes and hypertension are two of the commonest diseases in the world.As they unfavorably affect people of different age groups,they have become a cause of concern and must be predicted and diagnosed well in advance.Objective:This research aims to determine the effectiveness of artificial neural networks(ANNs)in predicting diabetes and blood pressure diseases and to point out the factors which have a high impact on these diseases.Sample:This work used two online datasets which consist of data collected from 768 individuals.We applied neural network algorithms to predict if the individuals have those two diseases based on some factors.Diabetes prediction is based on five factors:age,weight,fat-ratio,glucose,and insulin,while blood pressure prediction is based on six factors:age,weight,fat-ratio,blood pressure,alcohol,and smoking.Method:A model based on the Multi-Layer Perceptron Neural Network(MLP)was implemented.The inputs of the network were the factors for each disease,while the output was the prediction of the disease’s occurrence.The model performance was compared with other classifiers such as Support Vector Machine(SVM)and K-Nearest Neighbors(KNN).We used performance metrics measures to assess the accuracy and performance of MLP.Also,a tool was implemented to help diagnose the diseases and to understand the results.Result:The model predicted the two diseases with correct classification rate(CCR)of 77.6%for diabetes and 68.7%for hypertension.The results indicate that MLP correctly predicts the probability of being diseased or not,and the performance can be significantly increased compared with both SVM and KNN.This shows MLPs effectiveness in early disease prediction.展开更多
文摘A primary aortoenteric fistula (PAEF), defined as a communication between the native aorta and the gastrointestinal tract, is a rare cause of gastrointes-tinal bleeding. The preoperative diagnosis of PAEF is extremely difficult. Consequently, PAEF may cause sudden and unexpected death. We present an autopsy case of a 68-year-old man who died of massive gastro-intestinal bleeding due to a PAEF. Autopsy revealed a pinhole rupture located on the third part of the duode-nal mucosa and fistulized into the adjacent abdominal aortic aneurysm (AAA). Our case indicates that the aortoenteric fistula can result in fatal gastrointestinal bleeding. Consequently, a PAEF should be included in the differential diagnosis of gastrointestinal bleeding.
文摘Lycorine and galantamine are natural alkaloids found in Amaryllidaceae plants, such as narcissus. Narcissus leaves and roots are sometimes accidentally ingested because they resemble vegetables. Lycorine and galantamine are toxic and cause such effects as nausea, vomiting, and abdominal pain, when accidentally ingested. In a case of narcissus poisoning, the detection of lycorine and galantamine in biological samples is vital to determine whether they have been ingested. This study establishes a liquid chromatography-tandem mass spectrometry (LC/MS/MS) method to measure the lycorine and galantamine content of human serum, which can be used for mild to fatal poisoning cases. A serum pretreatment procedure was performed using acetonitrile and QuEChERS AOAC powder. The separation of the compounds was conducted using a pentafluorophenyl column, CAPCELL CORE PFP (2.1 mm I.D. × 100 mm, 2.7 μm). Lycorine, galantamine, and galantamine-d<sub>6</sub> (internal standard) were identified by the transitions of m/z 288 → 147, m/z 288 → 213, and m/z 294 → 216, respectively. The calibration curves were linear in the ranges of 0.05 to 5 ng/mL and 5 to 100 ng/mL, with R<sup>2</sup> > 0.999. The precision and accuracy were within the permissible range. The matrix effects of lycorine and galantamine were 94.3% - 98.4% and 87.8% - 91.1%, respectively. The extraction recovery rates of lycorine and galantamine were 101.9% - 112.7% and 95.6% - 107.1%, respectively. The present method detected lycorine and galantamine in the sera of three patients with mild poisoning that had accidentally ingested. This method is applicable in cases of lycorine and galantamine poisoning.
文摘Background:Diabetes and hypertension are two of the commonest diseases in the world.As they unfavorably affect people of different age groups,they have become a cause of concern and must be predicted and diagnosed well in advance.Objective:This research aims to determine the effectiveness of artificial neural networks(ANNs)in predicting diabetes and blood pressure diseases and to point out the factors which have a high impact on these diseases.Sample:This work used two online datasets which consist of data collected from 768 individuals.We applied neural network algorithms to predict if the individuals have those two diseases based on some factors.Diabetes prediction is based on five factors:age,weight,fat-ratio,glucose,and insulin,while blood pressure prediction is based on six factors:age,weight,fat-ratio,blood pressure,alcohol,and smoking.Method:A model based on the Multi-Layer Perceptron Neural Network(MLP)was implemented.The inputs of the network were the factors for each disease,while the output was the prediction of the disease’s occurrence.The model performance was compared with other classifiers such as Support Vector Machine(SVM)and K-Nearest Neighbors(KNN).We used performance metrics measures to assess the accuracy and performance of MLP.Also,a tool was implemented to help diagnose the diseases and to understand the results.Result:The model predicted the two diseases with correct classification rate(CCR)of 77.6%for diabetes and 68.7%for hypertension.The results indicate that MLP correctly predicts the probability of being diseased or not,and the performance can be significantly increased compared with both SVM and KNN.This shows MLPs effectiveness in early disease prediction.
