AIM:To compare visual field defects using the Swedish Interactive Thresholding Algorithm(SITA)Fast strategy with SITA Faster strategy,a newly developed time-saving threshold visual field strategy.METHODS:Ninety-three ...AIM:To compare visual field defects using the Swedish Interactive Thresholding Algorithm(SITA)Fast strategy with SITA Faster strategy,a newly developed time-saving threshold visual field strategy.METHODS:Ninety-three participants(60 glaucoma patients and 33 normal controls)were enrolled.One eye from each participant was selected randomly for the study.SITA Fast and SITA Faster were performed using the 24-2 default mode for each test.The differences of visual field defects between the two strategies were compared using the test duration,false-positive response errors,mean deviation(MD),visual field index(VFI)and the numbers of depressed test points at the significant levels of P<5%,<2%,<1%,and<0.5%in probability plots.The correlation between strategies was analyzed.The agreement between strategies was acquired by Bland-Altman analysis.RESULTS:Mean test durations were 246.0±60.9 s for SITA Fast,and 156.3±46.3 s for SITA Faster(P<0.001).The test duration of SITA Faster was 36.5%shorter than SITA Fast.The MD,VFI and numbers of depressed points at P<5%,<2%,<1%,and<0.5%in probability plots showed no statistically significant difference between two strategies(P>0.05).Correlation analysis showed a high correlation for MD(r=0.986,P<0.001)and VFI(r=0.986,P<0.001)between the two strategies.Bland-Altman analysis showed great agreement between the two strategies.CONCLUSION:SITA Faster,which saves considerable test time,has a great test quality comparing to SITA Fast,but may be not directly interchangeable.展开更多
Background: Fasting is a simple metabolic strategy that is used to estimate the maintenance energy requirement where the energy supply for basic physiological functions is provided by the mobilization of body reserves...Background: Fasting is a simple metabolic strategy that is used to estimate the maintenance energy requirement where the energy supply for basic physiological functions is provided by the mobilization of body reserves.However, the underlying metabolic components of maintenance energy expenditure are not clear. This study investigated the differences in heat production(HP), respiratory quotient(RQ) and plasma metabolites in pigs in the fed and fasted state, using the techniques of indirect calorimetry and metabolomics.Methods: Nine barrows(45.2 ± 1.7 kg BW) were fed corn-soybean based meal diets and were kept in metabolism crates for a period of 14 d. After 7 d adaptation, pigs were transferred to respiratory chambers to determine HP and RQ based on indirect calorimetry. Pigs were fed the diet at 2,400 k J ME/(kg BW0.6·d) during d 8 to 12. The last 2 d were divided into 24 h fasting and 48 h fasting treatment, respectively. Plasma samples of each pig were collected from the anterior vena cava during the last 3 d(1 d while pigs were fed and 2 d during which they were fasted).The metabolites of plasma were determined by high-resolution mass spectrometry using a metabolomics approach.Results: Indirect calorimetry analysis revealed that HP and RQ were no significant difference between 24 h fasting and 48 h fasting, which were lower than those of fed state(P < 0.01). The nitrogen concentration of urine tended to decrease with fasting(P = 0.054). Metabolomics analysis between the fed and fasted state revealed differences in15 compounds, most of which were not significantly different between 24 h fasting and 48 h fasting. Identified compounds were enriched in metabolic pathways related to linoleic acid metabolism, amino acid metabolism,sphingolipid metabolism, and pantothenate and Co A biosynthesis.Conclusion: These results suggest that the decreases in HP and RQ of growing pigs under fasting conditions were associated with the alterations of linoleic acid metabolism and amino acid metabolism. The integrative analysis also revealed that growing pigs under a 24-h fasting were more appropriate than a 48-h fasting to investigate the metabolic components of maintenance energy expenditure.展开更多
Type 2 diabetes (T2D) is characterised by defects in both fasting and postprandial glucose (PPG) control. Many Asians including Chinese people with T2D have isolated high PPG at diagnosis without concomitant elevation...Type 2 diabetes (T2D) is characterised by defects in both fasting and postprandial glucose (PPG) control. Many Asians including Chinese people with T2D have isolated high PPG at diagnosis without concomitant elevation in fasting plasma glucose (FPG). This might be due to differences in genetic architecture, low insulin secretion, and high carbohydrate diet in East Asians. Excursions in PPG contribute significantly to glycemic variability (GV).展开更多
Early identification and treatment of stroke can greatly improve patient outcomes and quality of life.Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are...Early identification and treatment of stroke can greatly improve patient outcomes and quality of life.Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are commonly used for stroke screening,accurate administration is dependent on specialized training.In this study,we proposed a novel multimodal deep learning approach,based on the FAST,for assessing suspected stroke patients exhibiting symptoms such as limb weakness,facial paresis,and speech disorders in acute settings.We collected a dataset comprising videos and audio recordings of emergency room patients performing designated limb movements,facial expressions,and speech tests based on the FAST.We compared the constructed deep learning model,which was designed to process multi-modal datasets,with six prior models that achieved good action classification performance,including the I3D,SlowFast,X3D,TPN,TimeSformer,and MViT.We found that the findings of our deep learning model had a higher clinical value compared with the other approaches.Moreover,the multi-modal model outperformed its single-module variants,highlighting the benefit of utilizing multiple types of patient data,such as action videos and speech audio.These results indicate that a multi-modal deep learning model combined with the FAST could greatly improve the accuracy and sensitivity of early stroke identification of stroke,thus providing a practical and powerful tool for assessing stroke patients in an emergency clinical setting.展开更多
文摘AIM:To compare visual field defects using the Swedish Interactive Thresholding Algorithm(SITA)Fast strategy with SITA Faster strategy,a newly developed time-saving threshold visual field strategy.METHODS:Ninety-three participants(60 glaucoma patients and 33 normal controls)were enrolled.One eye from each participant was selected randomly for the study.SITA Fast and SITA Faster were performed using the 24-2 default mode for each test.The differences of visual field defects between the two strategies were compared using the test duration,false-positive response errors,mean deviation(MD),visual field index(VFI)and the numbers of depressed test points at the significant levels of P<5%,<2%,<1%,and<0.5%in probability plots.The correlation between strategies was analyzed.The agreement between strategies was acquired by Bland-Altman analysis.RESULTS:Mean test durations were 246.0±60.9 s for SITA Fast,and 156.3±46.3 s for SITA Faster(P<0.001).The test duration of SITA Faster was 36.5%shorter than SITA Fast.The MD,VFI and numbers of depressed points at P<5%,<2%,<1%,and<0.5%in probability plots showed no statistically significant difference between two strategies(P>0.05).Correlation analysis showed a high correlation for MD(r=0.986,P<0.001)and VFI(r=0.986,P<0.001)between the two strategies.Bland-Altman analysis showed great agreement between the two strategies.CONCLUSION:SITA Faster,which saves considerable test time,has a great test quality comparing to SITA Fast,but may be not directly interchangeable.
基金financially supported by the National Natural Science Foundation of China(31372317)Developing Key Equipment for Digital Management and Monitoring Environment in Animal Production(2013AA10230602)+1 种基金Prevention and Control of Nutritional Metabolism and Toxic Diseases in Livestock and Poultry(2016YFD0501204)the 111 Project(B16044)
文摘Background: Fasting is a simple metabolic strategy that is used to estimate the maintenance energy requirement where the energy supply for basic physiological functions is provided by the mobilization of body reserves.However, the underlying metabolic components of maintenance energy expenditure are not clear. This study investigated the differences in heat production(HP), respiratory quotient(RQ) and plasma metabolites in pigs in the fed and fasted state, using the techniques of indirect calorimetry and metabolomics.Methods: Nine barrows(45.2 ± 1.7 kg BW) were fed corn-soybean based meal diets and were kept in metabolism crates for a period of 14 d. After 7 d adaptation, pigs were transferred to respiratory chambers to determine HP and RQ based on indirect calorimetry. Pigs were fed the diet at 2,400 k J ME/(kg BW0.6·d) during d 8 to 12. The last 2 d were divided into 24 h fasting and 48 h fasting treatment, respectively. Plasma samples of each pig were collected from the anterior vena cava during the last 3 d(1 d while pigs were fed and 2 d during which they were fasted).The metabolites of plasma were determined by high-resolution mass spectrometry using a metabolomics approach.Results: Indirect calorimetry analysis revealed that HP and RQ were no significant difference between 24 h fasting and 48 h fasting, which were lower than those of fed state(P < 0.01). The nitrogen concentration of urine tended to decrease with fasting(P = 0.054). Metabolomics analysis between the fed and fasted state revealed differences in15 compounds, most of which were not significantly different between 24 h fasting and 48 h fasting. Identified compounds were enriched in metabolic pathways related to linoleic acid metabolism, amino acid metabolism,sphingolipid metabolism, and pantothenate and Co A biosynthesis.Conclusion: These results suggest that the decreases in HP and RQ of growing pigs under fasting conditions were associated with the alterations of linoleic acid metabolism and amino acid metabolism. The integrative analysis also revealed that growing pigs under a 24-h fasting were more appropriate than a 48-h fasting to investigate the metabolic components of maintenance energy expenditure.
文摘Type 2 diabetes (T2D) is characterised by defects in both fasting and postprandial glucose (PPG) control. Many Asians including Chinese people with T2D have isolated high PPG at diagnosis without concomitant elevation in fasting plasma glucose (FPG). This might be due to differences in genetic architecture, low insulin secretion, and high carbohydrate diet in East Asians. Excursions in PPG contribute significantly to glycemic variability (GV).
基金supported by the Ministry of Science and Technology of China,No.2020AAA0109605(to XL)Meizhou Major Scientific and Technological Innovation PlatformsProjects of Guangdong Provincial Science & Technology Plan Projects,No.2019A0102005(to HW).
文摘Early identification and treatment of stroke can greatly improve patient outcomes and quality of life.Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are commonly used for stroke screening,accurate administration is dependent on specialized training.In this study,we proposed a novel multimodal deep learning approach,based on the FAST,for assessing suspected stroke patients exhibiting symptoms such as limb weakness,facial paresis,and speech disorders in acute settings.We collected a dataset comprising videos and audio recordings of emergency room patients performing designated limb movements,facial expressions,and speech tests based on the FAST.We compared the constructed deep learning model,which was designed to process multi-modal datasets,with six prior models that achieved good action classification performance,including the I3D,SlowFast,X3D,TPN,TimeSformer,and MViT.We found that the findings of our deep learning model had a higher clinical value compared with the other approaches.Moreover,the multi-modal model outperformed its single-module variants,highlighting the benefit of utilizing multiple types of patient data,such as action videos and speech audio.These results indicate that a multi-modal deep learning model combined with the FAST could greatly improve the accuracy and sensitivity of early stroke identification of stroke,thus providing a practical and powerful tool for assessing stroke patients in an emergency clinical setting.
