BACKGROUND： Acute poisoning （AP） may cause failure of the liver and kidney, and evendeath. This study aimed to investigate the efficacy of artificial liver support system （ALSS） on thetreatment of liver failure a...BACKGROUND： Acute poisoning （AP） may cause failure of the liver and kidney, and evendeath. This study aimed to investigate the efficacy of artificial liver support system （ALSS） on thetreatment of liver failure after acute poisoning.METHODS： A total of 31 patients with liver failure caused by AP were admitted to emergency ICU,central ICU, and Department of Gastroenterology from 2005 to 2009 in Zhongshan Hospital Affi liatedto Xiamen University, China. Among them, 13 patients served as a treatment group, and used ALSS inaddition to detoxifi cation treatment and protective treatment of liver function, and the other 18 patientsserved as a control group receiving detoxifi cation treatment and protective treatment of liver function.RESULTS： In the treatment group, 10 patients （76.9%） were cured or improved, 2 died, and1 was discharged against advice. In the 18 patients in the control group, 7 （38.9%） were cured orimproved, 3 died, and 8 were discharged against advice. There was a significant difference in therates of improvement between the two groups （P〈0.05）.CONCLUSION： ALSS is a safe and effective clinical method for the treatment of acute toxicliver failure.展开更多
Melanoma is of the lethal and rare types of skin cancer.It is curable at an initial stage and the patient can survive easily.It is very difficult to screen all skin lesion patients due to costly treatment.Clinicians ar...Melanoma is of the lethal and rare types of skin cancer.It is curable at an initial stage and the patient can survive easily.It is very difficult to screen all skin lesion patients due to costly treatment.Clinicians are requiring a correct method for the right treatment for dermoscopic clinical features such as lesion borders,pigment networks,and the color of melanoma.These challenges are required an automated system to classify the clinical features of melanoma and non-melanoma disease.The trained clinicians can overcome the issues such as low contrast,lesions varying in size,color,and the existence of several objects like hair,reflections,air bubbles,and oils on almost all images.Active contour is one of the suitable methods with some drawbacks for the segmentation of irre-gular shapes.An entropy and morphology-based automated mask selection is pro-posed for the active contour method.The proposed method can improve the overall segmentation along with the boundary of melanoma images.In this study,features have been extracted to perform the classification on different texture scales like Gray level co-occurrence matrix(GLCM)and Local binary pattern(LBP).When four different moments pull out in six different color spaces like HSV,Lin RGB,YIQ,YCbCr,XYZ,and CIE L*a*b then global information from different colors channels have been combined.Therefore,hybrid fused texture features;such as local,color feature as global,shape features,and Artificial neural network(ANN)as classifiers have been proposed for the categorization of the malignant and non-malignant.Experimentations had been carried out on datasets Dermis,DermQuest,and PH2.The results of our advanced method showed super-iority and contrast with the existing state-of-the-art techniques.展开更多
Rubber sheets are one of the primary products of natural rubber and are the main raw material in various rubber industries.The quality of a rubber sheet can be visually examined by holding it against clear light to in...Rubber sheets are one of the primary products of natural rubber and are the main raw material in various rubber industries.The quality of a rubber sheet can be visually examined by holding it against clear light to inspect for any specks and impurities inside,but its moisture content is difficult to evaluate based on a visual inspection and this might lead to unfair trading.Herein,we developed a rapid,robust and nondestructive near-infrared spectroscopy(NIRS)-based method for moisture content determination in rubber sheets.A set of 300 rubber sheets were divided into a calibration(200 samples)and prediction groups(100 samples).The calibration set was used to develop NIRS calibration equation using different calibration models,Partial Least Square Regression(PLSR),Least Square Support Vector Machine(LS-SVM)and Articial Neural Network(ANN).Among the models investigated,the ANN model with therst derivative of spectral preprocessing presented the best prediction with a coe±cient of determination(R^(2)_(P))of 0.993,root mean square error of calibration(RMSEC)of 0.126%and root mean square error of prediction(RMSEP)of 0.179%.The results indicated that the proposed NIRS-ANN model will be able to reduce human error and provide a highly accurate estimate of the moisture content in a rubber sheet compared to traditional wet chemistry estimation methods according to AOAC standards.展开更多
文摘BACKGROUND： Acute poisoning （AP） may cause failure of the liver and kidney, and evendeath. This study aimed to investigate the efficacy of artificial liver support system （ALSS） on thetreatment of liver failure after acute poisoning.METHODS： A total of 31 patients with liver failure caused by AP were admitted to emergency ICU,central ICU, and Department of Gastroenterology from 2005 to 2009 in Zhongshan Hospital Affi liatedto Xiamen University, China. Among them, 13 patients served as a treatment group, and used ALSS inaddition to detoxifi cation treatment and protective treatment of liver function, and the other 18 patientsserved as a control group receiving detoxifi cation treatment and protective treatment of liver function.RESULTS： In the treatment group, 10 patients （76.9%） were cured or improved, 2 died, and1 was discharged against advice. In the 18 patients in the control group, 7 （38.9%） were cured orimproved, 3 died, and 8 were discharged against advice. There was a significant difference in therates of improvement between the two groups （P〈0.05）.CONCLUSION： ALSS is a safe and effective clinical method for the treatment of acute toxicliver failure.
文摘Melanoma is of the lethal and rare types of skin cancer.It is curable at an initial stage and the patient can survive easily.It is very difficult to screen all skin lesion patients due to costly treatment.Clinicians are requiring a correct method for the right treatment for dermoscopic clinical features such as lesion borders,pigment networks,and the color of melanoma.These challenges are required an automated system to classify the clinical features of melanoma and non-melanoma disease.The trained clinicians can overcome the issues such as low contrast,lesions varying in size,color,and the existence of several objects like hair,reflections,air bubbles,and oils on almost all images.Active contour is one of the suitable methods with some drawbacks for the segmentation of irre-gular shapes.An entropy and morphology-based automated mask selection is pro-posed for the active contour method.The proposed method can improve the overall segmentation along with the boundary of melanoma images.In this study,features have been extracted to perform the classification on different texture scales like Gray level co-occurrence matrix(GLCM)and Local binary pattern(LBP).When four different moments pull out in six different color spaces like HSV,Lin RGB,YIQ,YCbCr,XYZ,and CIE L*a*b then global information from different colors channels have been combined.Therefore,hybrid fused texture features;such as local,color feature as global,shape features,and Artificial neural network(ANN)as classifiers have been proposed for the categorization of the malignant and non-malignant.Experimentations had been carried out on datasets Dermis,DermQuest,and PH2.The results of our advanced method showed super-iority and contrast with the existing state-of-the-art techniques.
基金supported by the Faculty of Engineering at Kamphaeng Saen,Kasetsart University,Thailand.
文摘Rubber sheets are one of the primary products of natural rubber and are the main raw material in various rubber industries.The quality of a rubber sheet can be visually examined by holding it against clear light to inspect for any specks and impurities inside,but its moisture content is difficult to evaluate based on a visual inspection and this might lead to unfair trading.Herein,we developed a rapid,robust and nondestructive near-infrared spectroscopy(NIRS)-based method for moisture content determination in rubber sheets.A set of 300 rubber sheets were divided into a calibration(200 samples)and prediction groups(100 samples).The calibration set was used to develop NIRS calibration equation using different calibration models,Partial Least Square Regression(PLSR),Least Square Support Vector Machine(LS-SVM)and Articial Neural Network(ANN).Among the models investigated,the ANN model with therst derivative of spectral preprocessing presented the best prediction with a coe±cient of determination(R^(2)_(P))of 0.993,root mean square error of calibration(RMSEC)of 0.126%and root mean square error of prediction(RMSEP)of 0.179%.The results indicated that the proposed NIRS-ANN model will be able to reduce human error and provide a highly accurate estimate of the moisture content in a rubber sheet compared to traditional wet chemistry estimation methods according to AOAC standards.
