Background:Sleep disturbance is commonly seen in fibromyalgia syndrome (FMS);however,high quality studies involving manual therapies that target FMS-linked poor sleep quality are lacking for the Indian population.Obje...Background:Sleep disturbance is commonly seen in fibromyalgia syndrome (FMS);however,high quality studies involving manual therapies that target FMS-linked poor sleep quality are lacking for the Indian population.Objective:Craniosacral therapy (CST),Bowen therapy and exercises have been found to influence the autonomic nervous system,which plays a crucial role in sleep physiology.Given the paucity of evidence concerning these effects in individuals with FMS,our study tests the effectiveness of CST,Bowen therapy and a standard exercise program against static touch (the manual placebo group) on sleep quality in FMS.Design,setting,participants and intervention:A placebo-controlled randomized trial was conducted on132 FMS participants with poor sleep at a hospital in Bangalore.The participants were randomly allocated to one of the four study groups,including CST,Bowen therapy,standard exercise program,and a manual placebo control group that received static touch.CST,Bowen therapy and static touch treatments were administered in once-weekly 45-minute sessions for 12 weeks;the standard exercise group received weekly supervised exercises for 6 weeks with home exercises until 12 weeks.After 12 weeks,all study participants performed the standard exercises at home for another 12 weeks.Main outcome measures:Sleep quality,pressure pain threshold (PPT),quality of life and fibromyalgia impact,physical function,fatigue,pain catastrophizing,kinesiophobia,and positive–negative affect were recorded at baseline,and at weeks 12 and 24 of the intervention.Results:At the end of 12 weeks,the sleep quality improved significantly in the CST group (P=0.037) and Bowen therapy group (P=0.023),and the PPT improved significantly in the Bowen therapy group(P=0.002) and the standard exercise group (P<0.001),compared to the static touch group.These improvements were maintained at 24 weeks.No between-group differences were observed for other secondary outcomes.Conclusion:CST and Bowen therapy improved sleep quality,and Bowen therapy and standard exercises improved pain threshold in the short term.These improvements were retained within the groups in the long term by adding exercises.CST and Bowen therapy are treatment options to improve sleep and reduce pain in FMS.展开更多
As we set into the second half of 2022,the world is still recovering from the two-year COVID-19 pandemic.However,over the past three months,the outbreak of the Monkeypox Virus(MPV)has led to fifty-two thousand confirm...As we set into the second half of 2022,the world is still recovering from the two-year COVID-19 pandemic.However,over the past three months,the outbreak of the Monkeypox Virus(MPV)has led to fifty-two thousand confirmed cases and over one hundred deaths.This caused the World Health Organisation to declare the outbreak a Public Health Emergency of International Concern(PHEIC).If this outbreak worsens,we could be looking at the Monkeypox virus causing the next global pandemic.As Monkeypox affects the human skin,the symptoms can be captured with regular imaging.Large samples of these images can be used as a training dataset for machine learning-based detection tools.Using a regular camera to capture the skin image of the infected person and running it against computer vision models is beneficial.In this research,we use deep learning to diagnose monkeypox from skin lesion images.Using a publicly available dataset,we tested the dataset on five pre-trained deep neural networks:GoogLeNet,Places365-GoogLeNet,SqueezeNet,AlexNet and ResNet-18.Hyperparameter was done to choose the best parameters.Performance metrics such as accuracy,precision,recall,f1-score and AUC were considered.Among the above models,ResNet18 was able to obtain the highest accuracy of 99.49%.The modified models obtained validation accuracies above 95%.The results prove that deep learning models such as the proposed model based on ResNet-18 can be deployed and can be crucial in battling the monkeypox virus.Since the used networks are optimized for efficiency,they can be used on performance limited devices such as smartphones with cameras.The addition of explainable artificial intelligence techniques LIME and GradCAM enables visual interpretation of the prediction made,helping health professionals using the model.展开更多
文摘Background:Sleep disturbance is commonly seen in fibromyalgia syndrome (FMS);however,high quality studies involving manual therapies that target FMS-linked poor sleep quality are lacking for the Indian population.Objective:Craniosacral therapy (CST),Bowen therapy and exercises have been found to influence the autonomic nervous system,which plays a crucial role in sleep physiology.Given the paucity of evidence concerning these effects in individuals with FMS,our study tests the effectiveness of CST,Bowen therapy and a standard exercise program against static touch (the manual placebo group) on sleep quality in FMS.Design,setting,participants and intervention:A placebo-controlled randomized trial was conducted on132 FMS participants with poor sleep at a hospital in Bangalore.The participants were randomly allocated to one of the four study groups,including CST,Bowen therapy,standard exercise program,and a manual placebo control group that received static touch.CST,Bowen therapy and static touch treatments were administered in once-weekly 45-minute sessions for 12 weeks;the standard exercise group received weekly supervised exercises for 6 weeks with home exercises until 12 weeks.After 12 weeks,all study participants performed the standard exercises at home for another 12 weeks.Main outcome measures:Sleep quality,pressure pain threshold (PPT),quality of life and fibromyalgia impact,physical function,fatigue,pain catastrophizing,kinesiophobia,and positive–negative affect were recorded at baseline,and at weeks 12 and 24 of the intervention.Results:At the end of 12 weeks,the sleep quality improved significantly in the CST group (P=0.037) and Bowen therapy group (P=0.023),and the PPT improved significantly in the Bowen therapy group(P=0.002) and the standard exercise group (P<0.001),compared to the static touch group.These improvements were maintained at 24 weeks.No between-group differences were observed for other secondary outcomes.Conclusion:CST and Bowen therapy improved sleep quality,and Bowen therapy and standard exercises improved pain threshold in the short term.These improvements were retained within the groups in the long term by adding exercises.CST and Bowen therapy are treatment options to improve sleep and reduce pain in FMS.
文摘As we set into the second half of 2022,the world is still recovering from the two-year COVID-19 pandemic.However,over the past three months,the outbreak of the Monkeypox Virus(MPV)has led to fifty-two thousand confirmed cases and over one hundred deaths.This caused the World Health Organisation to declare the outbreak a Public Health Emergency of International Concern(PHEIC).If this outbreak worsens,we could be looking at the Monkeypox virus causing the next global pandemic.As Monkeypox affects the human skin,the symptoms can be captured with regular imaging.Large samples of these images can be used as a training dataset for machine learning-based detection tools.Using a regular camera to capture the skin image of the infected person and running it against computer vision models is beneficial.In this research,we use deep learning to diagnose monkeypox from skin lesion images.Using a publicly available dataset,we tested the dataset on five pre-trained deep neural networks:GoogLeNet,Places365-GoogLeNet,SqueezeNet,AlexNet and ResNet-18.Hyperparameter was done to choose the best parameters.Performance metrics such as accuracy,precision,recall,f1-score and AUC were considered.Among the above models,ResNet18 was able to obtain the highest accuracy of 99.49%.The modified models obtained validation accuracies above 95%.The results prove that deep learning models such as the proposed model based on ResNet-18 can be deployed and can be crucial in battling the monkeypox virus.Since the used networks are optimized for efficiency,they can be used on performance limited devices such as smartphones with cameras.The addition of explainable artificial intelligence techniques LIME and GradCAM enables visual interpretation of the prediction made,helping health professionals using the model.
