The rodent running-wheel recording apparatus is a reliable approach for studying cir-cadian rhythm.This study demonstrated how to construct a simple and intelligent running-wheel recording system.The running wheel was...The rodent running-wheel recording apparatus is a reliable approach for studying cir-cadian rhythm.This study demonstrated how to construct a simple and intelligent running-wheel recording system.The running wheel was attached to the cage's base,whereas the Hall sensor was attached to the cage's cover.Then,the RJ25 adaptor relayed the running signal to the main control board.Finally,the main control board was connected to the USB port of the computer with the USB connection.Data were collected using the online-accessible,self-created software Magturning.Through Magturning,generated data were saved and exported in real time.Afterward,the device was validated by collecting data on the locomotor activities of mice under dif-ferent light conditions.In conclusion,this new device can record circadian activity of rodents.Our device is appropriate for interdisciplinary investigations related to biological clock research.展开更多
In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and recovery.The exploitation of machine learning with an intelligent agent in the area of health infor...In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and recovery.The exploitation of machine learning with an intelligent agent in the area of health informatics gathered using HAR augments the decision-making quality and significance.Although many research works conducted on Smart Healthcare Monitoring,there remain a certain number of pitfalls such as time,overhead,and falsification involved during analysis.Therefore,this paper proposes a Statistical Partial Regression and Support Vector Intelligent Agent Learning(SPR-SVIAL)for Smart Healthcare Monitoring.At first,the Statistical Partial Regression Feature Extraction model is used for data preprocessing along with the dimensionality-reduced features extraction process.Here,the input dataset the continuous beat-to-beat heart data,triaxial accelerometer data,and psychological characteristics were acquired from IoT wearable devices.To attain highly accurate Smart Healthcare Monitoring with less time,Partial Least Square helps extract the dimensionality-reduced features.After that,with these resulting features,SVIAL is proposed for Smart Healthcare Monitoring with the help of Machine Learning and Intelligent Agents to minimize both analysis falsification and overhead.Experimental evaluation is carried out for factors such as time,overhead,and false positive rate accuracy concerning several instances.The quantitatively analyzed results indicate the better performance of our proposed SPR-SVIAL method when compared with two state-of-the-art methods.展开更多
Smoking is a major cause of cancer,heart disease and other afflictions that lead to early mortality.An effective smoking classification mechanism that provides insights into individual smoking habits would assist in i...Smoking is a major cause of cancer,heart disease and other afflictions that lead to early mortality.An effective smoking classification mechanism that provides insights into individual smoking habits would assist in implementing addiction treatment initiatives.Smoking activities often accompany other activities such as drinking or eating.Consequently,smoking activity recognition can be a challenging topic in human activity recognition(HAR).A deep learning framework for smoking activity recognition(SAR)employing smartwatch sensors was proposed together with a deep residual network combined with squeeze-and-excitation modules(ResNetSE)to increase the effectiveness of the SAR framework.The proposed model was tested against basic convolutional neural networks(CNNs)and recurrent neural networks(LSTM,BiLSTM,GRU and BiGRU)to recognize smoking and other similar activities such as drinking,eating and walking using the UT-Smoke dataset.Three different scenarios were investigated for their recognition performances using standard HAR metrics(accuracy,F1-score and the area under the ROC curve).Our proposed ResNetSE outperformed the other basic deep learning networks,with maximum accuracy of 98.63%.展开更多
Background:Information on the association between physical activity(PA)and the risk of chronic kidney disease(CKD)is limited.We aimed to explore the associations of total,domain-specific,and intensity-specific PA with...Background:Information on the association between physical activity(PA)and the risk of chronic kidney disease(CKD)is limited.We aimed to explore the associations of total,domain-specific,and intensity-specific PA with CKD and its subtypes in China.Methods:The study included 475,376 adults from the China Kadoorie Biobank aged 30-79 years during 2004-2008 at baseline.An interviewer-administered questionnaire was used to collect the information about PA,which was quantified as metabolic equivalent of task hours per day(MET-h/day)and categorized into 4 groups based on quartiles.Cox regression was used to analyze the association between PA and CKD risk.Results:During a median follow-up of 12.1 years,5415 incident CKD cases were documented,including 1159 incident diabetic kidney disease(DKD)cases and 362 incident hypertensive nephropathy(HTN)cases.Total PA was inversely associated with CKD risk,with an adjusted hazard ratio(HR,95%confidence interval(95%CI))of 0.83(0.75-0.92)for incident CKD in the highest quartile of total PA as compared with participants in the lowest quartile.Similar results were observed for risk of DKD and HTN,and the corresponding HRs(95%CIs)were 0.75(0.58-0.97)for DKD risk and 0.56(0.37-0.85)for HTN risk.Increased nonoccupational PA,low-intensity PA,and moderate-to-vigorous-intensity PA were significantly associated with a decreased risk of CKD,with HRs(95%CIs)of 0.80(0.73-0.88),0.85(0.77-0.94),and 0.85(0.76-0.95)in the highest quartile,respectively.Conclusion:PA,including nonoccupational PA,low-intensity PA,and moderate-to-vigorous-intensity PA,was inversely associated with the risk of CKD,including DKD,HTN,and other CKD,and such associations were dose dependent.展开更多
Background:Prolonged sitting and reduced physical activity lead to low energy expenditures.However,little is known about the joint impact of daily sitting time and physical activity on body fat distribution.We investi...Background:Prolonged sitting and reduced physical activity lead to low energy expenditures.However,little is known about the joint impact of daily sitting time and physical activity on body fat distribution.We investigated the independent and joint associations of daily sitting time and physical activity with body fat among adults.Methods:This was a cross-sectional analysis of U.S.nationally representative data from the National Health and Nutrition Examination Survey2011-2018 among adults aged 20 years or older.Daily sitting time and leisure-time physical activity(LTPA)were self-reported using the Global Physical Activity Questionnaire.Body fat(total and trunk fat percentage)was determined via dual X-ray absorptiometry.Results:Among 10,808 adults,about 54.6%spent 6 h/day or more sitting;more than one-half reported no LTPA(inactive)or less than 150 min/week LTPA(insufficiently active)with only 43.3%reported 150 min/week or more LTPA(active)in the past week.After fully adjusting for sociodemographic data,lifestyle behaviors,and chronic conditions,prolonged sitting time and low levels of LTPA were associated with higher total and trunk fat percentages in both sexes.When stratifying by LTPA,the association between daily sitting time and body fat appeared to be stronger in those who were inactive/insuufficiently active.In the joint analyses,inactive/insuufficiently active adults who reported sitting more than 8 h/day had the highest total(female:3.99%(95%confidence interval(95%CI):3.09%-4.88%);male:3.79%(95%CI:2.75%-4.82%))and trunk body fat percentages(female:4.21%(95%CI:3.09%-5.32%);male:4.07%(95%CI:2.95%-5.19%))when compared with those who were active and sitting less than 4 h/day.Conclusion:Prolonged daily sitting time was associated with increased body fat among U.S.adults.The higher body fat associated with 6 h/day sitting may not be offset by achieving recommended levels of physical activity.展开更多
In this work,the antibacterial activity of cotton containing silver nanocapsules prepared by atmospheric pressure plasma(APP)deposition is investigated.The nanocapsules consist of a shell and a silver nanoparticle(Ag ...In this work,the antibacterial activity of cotton containing silver nanocapsules prepared by atmospheric pressure plasma(APP)deposition is investigated.The nanocapsules consist of a shell and a silver nanoparticle(Ag NP)core,where the core is used to bring antibacterial activity,and the shell is utilized to suppress the potential toxicity of Ag NPs.The surface morphology and the elements of the samples are analyzed by scanning electron microscopy(SEM),energy dispersive x-ray and x-ray photoelectron spectroscopy(XPS).The SEM results show that the skin of the cotton fibers will fall off gradually after APP treatment over 3 min,and the XPS results show that the Ag content will rise to 1.6%after APP deposition for 10 min.Furthermore,the antimicrobial activity tests show that the reduction rates of Escherichia coli and Staphylococcus aureus can achieve 100%when the sample is treated for 10 min,which exhibits excellent antibacterial activity.In addition,the UV absorption properties of the cotton will also be correspondingly improved,which brings a broader application prospect for antibacterial cotton.展开更多
In this editorial we comment on the article titled“Inflammatory bowel diseases patients suffer from significant low levels and barriers to physical activity:The BE-FIT-IBD study”published in a recent issue of the Wo...In this editorial we comment on the article titled“Inflammatory bowel diseases patients suffer from significant low levels and barriers to physical activity:The BE-FIT-IBD study”published in a recent issue of the World Journal of Gastroen-terology 2023;29(41):5668-5682.Inflammatory bowel diseases(IBD)are emerging as a significant global health concern as their incidence continues to rise on a global scale,with detrimental impacts on quality of life.While many advances have been made regarding the management of the disease,physical inactivity in these patients represents an underexplored issue that may hold the key for further and better understanding the ramifications of IBD.Chronic pain,fatigue,and fear of exacerbating symptoms promotes physical inactivity among IBD patients,while the lack of clear guidelines on safe exercise regimens contributes to a norm of physical inactivity.Physical activity(PA)is accepted to have a positive effect on disease outcomes and quality of life,while inactivity exacerbates comorbidities like cardiovascular disease and mental health disorders.The“BE-FIT-IBD”study,focusing on PA levels and barriers in IBD patients of Southern Italy,revealed that a significant proportion(42.9%)were physically inactive.This lack of PA is attributed to barriers such as fear of flare-ups and misconceptions about exercise exacerbating the disease.The study also highlighted the need for better communication between healthcare providers and patients regarding the benefits of PA and safe incorporation into lifestyles.Moreover,physical inactivity may also contribute to disability in IBD patients,having a great impact on employment status.Of note is the fact that IBD also comes with an important psychological burden with relevant evidence suggesting that regular PA can improve mood,reduce anxiety,and enhance mental health.The“BE-FIT-IBD”study advocated for the integration of PA into IBD management,emphasizing the bidirectional link between PA and IBD.Regular exercise can influence the course of IBD,potentially reducing symptom severity and prolonging remission periods.As such,it is mandatory that healthcare providers actively educate patients,dispel misconceptions,and tailor exercise recommendations to improve the quality of life and reduce IBD-related complications.展开更多
Plant-based fermentations provide an untapped source for novel biotechnological applications.In this study,a probiotic named Lactobacillus fermentum 21828 was introduced to ferment Lentinus edodes.Polysaccharides were...Plant-based fermentations provide an untapped source for novel biotechnological applications.In this study,a probiotic named Lactobacillus fermentum 21828 was introduced to ferment Lentinus edodes.Polysaccharides were extracted from fermented and non-fermented L.edodes and purified via DEAE-52 and Sephadex G-100.The components designated F-LEP-2a and NF-LEP-2a were analyzed by FT-IR,HPGPC,HPAEC,SEM,GC-MS and NMR.The results revealed that probiotic fermentation increased the molecular weight from 1.16×10^(4) Da to 1.87×10^(4) Da and altered the proportions of glucose,galactose and mannose,in which glucose increased from 45.94%to 48.16%.Methylation analysis and NMR spectra indicated that F-LEP-2a and NF-LEP-2a had similar linkage patterns.Furthermore,their immunomodulatory activities were evaluated with immunosuppressive mice.NF-LEP and F-LEP improved immune organ indices,immunoglobulin(Ig G and Ig M)and cytokines concentrations;restored the antioxidation capacity of liver;and maintained the balance of gut microbiota.F-LEP displayed better moderating effects on the spleen index,immunoglobulin,cytokines and the diversity of gut microbiota than NF-LEP(200,400 mg/kg).Our study provides an efficient and environment-friendly way for the structural modification of polysaccharides,which helps to enhance their biological activity and promote their wide application in food,medicine and other fields.展开更多
Accidents are still an issue in an intelligent transportation system,despite developments in self-driving technology(ITS).Drivers who engage in risky behavior account for more than half of all road accidents.As a resu...Accidents are still an issue in an intelligent transportation system,despite developments in self-driving technology(ITS).Drivers who engage in risky behavior account for more than half of all road accidents.As a result,reckless driving behaviour can cause congestion and delays.Computer vision and multimodal sensors have been used to study driving behaviour categorization to lessen this problem.Previous research has also collected and analyzed a wide range of data,including electroencephalography(EEG),electrooculography(EOG),and photographs of the driver’s face.On the other hand,driving a car is a complicated action that requires a wide range of body move-ments.In this work,we proposed a ResNet-SE model,an efficient deep learning classifier for driving activity clas-sification based on signal data obtained in real-world traffic conditions using smart glasses.End-to-end learning can be achieved by combining residual networks and channel attention approaches into a single learning model.Sensor data from 3-point EOG electrodes,tri-axial accelerometer,and tri-axial gyroscope from the Smart Glasses dataset was utilized in this study.We performed various experiments and compared the proposed model to base-line deep learning algorithms(CNNs and LSTMs)to demonstrate its performance.According to the research results,the proposed model outperforms the previous deep learning models in this domain with an accuracy of 99.17%and an F1-score of 98.96%.展开更多
Lawsonia inermis is a hairless plant growing in various regions of North Africa, the Indian subcontinent, and the Middle East. It possesses many medicinal attributes, including curative properties against infectious d...Lawsonia inermis is a hairless plant growing in various regions of North Africa, the Indian subcontinent, and the Middle East. It possesses many medicinal attributes, including curative properties against infectious dermatoses. This study was carried out to evaluate the phytochemical profile of the crude ethanolic extract of the plant leaves and its fractions as well as their antimicrobial activities. The phytochemical profile was performed using high-performance thin-layer chromatography (HPTLC), gas chromatography-mass spectrometry (GC-MS), and high-performance liquid chromatography (HPLC). Additionally, the phenolic and flavonoid contents were determined using the Folin-Ciocalteu spectrophotometric and the aluminum trichloride methods. Antimicrobial activity was tested using disc diffusion and microdilution methods. The presence of flavonoids, tannins, sterols, and triterpenes was revealed. GC-MS detected twelve compounds main compounds consisting of saturated and unsaturated fatty acids and phenolic and terpenoid compounds among twenty-seven components. HPLC also detected high contents of phenolic acids and flavonoids. The most abundant triterpene and sterols were ursolic acid (around 43.14 g/100g DW, 13.9 g/100g dry weight (DW), and 0.68 g/100g DW) in the crude ethanolic extract of leaves (FeLi), hexane fraction (FHLi) and dichloromethane fraction (FDLi), respectively and, β-sitosterol in FeLi (56.7 mg/100g DW), FHLi (10.55 g/100g DW), FDLi (106.1 mg/100g DW) and butanol fraction (FBLi) (357.4 mg/100g DW). Among the flavonoids, rutin = 3.24 g/100g and quercetin = 0.63 g/100g in the ethanolic extract, rutin = 15.73 g/100g in the dichloromethane fraction, and rutin = 0.23 g/100g) in the aqueous fraction;and among phenolic compounds, caffeic acid (37.65 g/100g DW) and vanillic acid (22.70 g/100g DW) were the most important in the ethyl acetate fraction (FAeLi). All organic fractions exhibited interesting antibacterial and antifungal activities against the tested strains, with the best activity recorded with the dichloromethane and ethyl acetate fractions. The leaf extracts’ phytochemical profile and antimicrobial activity support the use of Lawsonia inermis against infectious skin diseases.展开更多
基金Startup Fund for scientific research,Fujian Medical University,Grant/Award Number:2020QH1039Joint Funds for the Innovation of Science and Technology,Fujian Province,Grant/Award Number:2020Y9114 and 2020Y9119。
文摘The rodent running-wheel recording apparatus is a reliable approach for studying cir-cadian rhythm.This study demonstrated how to construct a simple and intelligent running-wheel recording system.The running wheel was attached to the cage's base,whereas the Hall sensor was attached to the cage's cover.Then,the RJ25 adaptor relayed the running signal to the main control board.Finally,the main control board was connected to the USB port of the computer with the USB connection.Data were collected using the online-accessible,self-created software Magturning.Through Magturning,generated data were saved and exported in real time.Afterward,the device was validated by collecting data on the locomotor activities of mice under dif-ferent light conditions.In conclusion,this new device can record circadian activity of rodents.Our device is appropriate for interdisciplinary investigations related to biological clock research.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R194)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and recovery.The exploitation of machine learning with an intelligent agent in the area of health informatics gathered using HAR augments the decision-making quality and significance.Although many research works conducted on Smart Healthcare Monitoring,there remain a certain number of pitfalls such as time,overhead,and falsification involved during analysis.Therefore,this paper proposes a Statistical Partial Regression and Support Vector Intelligent Agent Learning(SPR-SVIAL)for Smart Healthcare Monitoring.At first,the Statistical Partial Regression Feature Extraction model is used for data preprocessing along with the dimensionality-reduced features extraction process.Here,the input dataset the continuous beat-to-beat heart data,triaxial accelerometer data,and psychological characteristics were acquired from IoT wearable devices.To attain highly accurate Smart Healthcare Monitoring with less time,Partial Least Square helps extract the dimensionality-reduced features.After that,with these resulting features,SVIAL is proposed for Smart Healthcare Monitoring with the help of Machine Learning and Intelligent Agents to minimize both analysis falsification and overhead.Experimental evaluation is carried out for factors such as time,overhead,and false positive rate accuracy concerning several instances.The quantitatively analyzed results indicate the better performance of our proposed SPR-SVIAL method when compared with two state-of-the-art methods.
基金support provided by Thammasat University Research fund under the TSRI,Contract No.TUFF19/2564 and TUFF24/2565,for the project of“AI Ready City Networking in RUN”,based on the RUN Digital Cluster collaboration schemeThis research project was also supported by the Thailand Science Research and Innonation fund,the University of Phayao(Grant No.FF65-RIM041)supported by King Mongkut’s University of Technology North Bangkok,Contract No.KMUTNB-65-KNOW-02.
文摘Smoking is a major cause of cancer,heart disease and other afflictions that lead to early mortality.An effective smoking classification mechanism that provides insights into individual smoking habits would assist in implementing addiction treatment initiatives.Smoking activities often accompany other activities such as drinking or eating.Consequently,smoking activity recognition can be a challenging topic in human activity recognition(HAR).A deep learning framework for smoking activity recognition(SAR)employing smartwatch sensors was proposed together with a deep residual network combined with squeeze-and-excitation modules(ResNetSE)to increase the effectiveness of the SAR framework.The proposed model was tested against basic convolutional neural networks(CNNs)and recurrent neural networks(LSTM,BiLSTM,GRU and BiGRU)to recognize smoking and other similar activities such as drinking,eating and walking using the UT-Smoke dataset.Three different scenarios were investigated for their recognition performances using standard HAR metrics(accuracy,F1-score and the area under the ROC curve).Our proposed ResNetSE outperformed the other basic deep learning networks,with maximum accuracy of 98.63%.
基金supported by National Natural Science Foundation of China(82192900,82192901,82192904,81941018,and 91846303)Peking University Medicine Seed Fund for Interdisciplinary Research(BMU2022MX025)+5 种基金the Fundamental Research Funds for the Central Universitiessupported by a grant from the Kadoorie Charitable Foundation in Hong Kongsupported by grants from the UK Wellcome Trust(212946/Z/18/Z,202922/Z/16/Z,104085/Z/14/Z,and 088158/Z/09/Z)the National Key R&D Program of China(2016YFC0900500)National Natural Science Foundation of China(81390540)Chinese Ministry of Science and Technology(2011BAI09B01)。
文摘Background:Information on the association between physical activity(PA)and the risk of chronic kidney disease(CKD)is limited.We aimed to explore the associations of total,domain-specific,and intensity-specific PA with CKD and its subtypes in China.Methods:The study included 475,376 adults from the China Kadoorie Biobank aged 30-79 years during 2004-2008 at baseline.An interviewer-administered questionnaire was used to collect the information about PA,which was quantified as metabolic equivalent of task hours per day(MET-h/day)and categorized into 4 groups based on quartiles.Cox regression was used to analyze the association between PA and CKD risk.Results:During a median follow-up of 12.1 years,5415 incident CKD cases were documented,including 1159 incident diabetic kidney disease(DKD)cases and 362 incident hypertensive nephropathy(HTN)cases.Total PA was inversely associated with CKD risk,with an adjusted hazard ratio(HR,95%confidence interval(95%CI))of 0.83(0.75-0.92)for incident CKD in the highest quartile of total PA as compared with participants in the lowest quartile.Similar results were observed for risk of DKD and HTN,and the corresponding HRs(95%CIs)were 0.75(0.58-0.97)for DKD risk and 0.56(0.37-0.85)for HTN risk.Increased nonoccupational PA,low-intensity PA,and moderate-to-vigorous-intensity PA were significantly associated with a decreased risk of CKD,with HRs(95%CIs)of 0.80(0.73-0.88),0.85(0.77-0.94),and 0.85(0.76-0.95)in the highest quartile,respectively.Conclusion:PA,including nonoccupational PA,low-intensity PA,and moderate-to-vigorous-intensity PA,was inversely associated with the risk of CKD,including DKD,HTN,and other CKD,and such associations were dose dependent.
文摘Background:Prolonged sitting and reduced physical activity lead to low energy expenditures.However,little is known about the joint impact of daily sitting time and physical activity on body fat distribution.We investigated the independent and joint associations of daily sitting time and physical activity with body fat among adults.Methods:This was a cross-sectional analysis of U.S.nationally representative data from the National Health and Nutrition Examination Survey2011-2018 among adults aged 20 years or older.Daily sitting time and leisure-time physical activity(LTPA)were self-reported using the Global Physical Activity Questionnaire.Body fat(total and trunk fat percentage)was determined via dual X-ray absorptiometry.Results:Among 10,808 adults,about 54.6%spent 6 h/day or more sitting;more than one-half reported no LTPA(inactive)or less than 150 min/week LTPA(insufficiently active)with only 43.3%reported 150 min/week or more LTPA(active)in the past week.After fully adjusting for sociodemographic data,lifestyle behaviors,and chronic conditions,prolonged sitting time and low levels of LTPA were associated with higher total and trunk fat percentages in both sexes.When stratifying by LTPA,the association between daily sitting time and body fat appeared to be stronger in those who were inactive/insuufficiently active.In the joint analyses,inactive/insuufficiently active adults who reported sitting more than 8 h/day had the highest total(female:3.99%(95%confidence interval(95%CI):3.09%-4.88%);male:3.79%(95%CI:2.75%-4.82%))and trunk body fat percentages(female:4.21%(95%CI:3.09%-5.32%);male:4.07%(95%CI:2.95%-5.19%))when compared with those who were active and sitting less than 4 h/day.Conclusion:Prolonged daily sitting time was associated with increased body fat among U.S.adults.The higher body fat associated with 6 h/day sitting may not be offset by achieving recommended levels of physical activity.
基金supported by National Natural Science Foundation of China(Nos.11975163 and 12175160)together with a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)。
文摘In this work,the antibacterial activity of cotton containing silver nanocapsules prepared by atmospheric pressure plasma(APP)deposition is investigated.The nanocapsules consist of a shell and a silver nanoparticle(Ag NP)core,where the core is used to bring antibacterial activity,and the shell is utilized to suppress the potential toxicity of Ag NPs.The surface morphology and the elements of the samples are analyzed by scanning electron microscopy(SEM),energy dispersive x-ray and x-ray photoelectron spectroscopy(XPS).The SEM results show that the skin of the cotton fibers will fall off gradually after APP treatment over 3 min,and the XPS results show that the Ag content will rise to 1.6%after APP deposition for 10 min.Furthermore,the antimicrobial activity tests show that the reduction rates of Escherichia coli and Staphylococcus aureus can achieve 100%when the sample is treated for 10 min,which exhibits excellent antibacterial activity.In addition,the UV absorption properties of the cotton will also be correspondingly improved,which brings a broader application prospect for antibacterial cotton.
文摘In this editorial we comment on the article titled“Inflammatory bowel diseases patients suffer from significant low levels and barriers to physical activity:The BE-FIT-IBD study”published in a recent issue of the World Journal of Gastroen-terology 2023;29(41):5668-5682.Inflammatory bowel diseases(IBD)are emerging as a significant global health concern as their incidence continues to rise on a global scale,with detrimental impacts on quality of life.While many advances have been made regarding the management of the disease,physical inactivity in these patients represents an underexplored issue that may hold the key for further and better understanding the ramifications of IBD.Chronic pain,fatigue,and fear of exacerbating symptoms promotes physical inactivity among IBD patients,while the lack of clear guidelines on safe exercise regimens contributes to a norm of physical inactivity.Physical activity(PA)is accepted to have a positive effect on disease outcomes and quality of life,while inactivity exacerbates comorbidities like cardiovascular disease and mental health disorders.The“BE-FIT-IBD”study,focusing on PA levels and barriers in IBD patients of Southern Italy,revealed that a significant proportion(42.9%)were physically inactive.This lack of PA is attributed to barriers such as fear of flare-ups and misconceptions about exercise exacerbating the disease.The study also highlighted the need for better communication between healthcare providers and patients regarding the benefits of PA and safe incorporation into lifestyles.Moreover,physical inactivity may also contribute to disability in IBD patients,having a great impact on employment status.Of note is the fact that IBD also comes with an important psychological burden with relevant evidence suggesting that regular PA can improve mood,reduce anxiety,and enhance mental health.The“BE-FIT-IBD”study advocated for the integration of PA into IBD management,emphasizing the bidirectional link between PA and IBD.Regular exercise can influence the course of IBD,potentially reducing symptom severity and prolonging remission periods.As such,it is mandatory that healthcare providers actively educate patients,dispel misconceptions,and tailor exercise recommendations to improve the quality of life and reduce IBD-related complications.
基金supported by grants from the National Key R&D Program of China(2019YFC1606701)。
文摘Plant-based fermentations provide an untapped source for novel biotechnological applications.In this study,a probiotic named Lactobacillus fermentum 21828 was introduced to ferment Lentinus edodes.Polysaccharides were extracted from fermented and non-fermented L.edodes and purified via DEAE-52 and Sephadex G-100.The components designated F-LEP-2a and NF-LEP-2a were analyzed by FT-IR,HPGPC,HPAEC,SEM,GC-MS and NMR.The results revealed that probiotic fermentation increased the molecular weight from 1.16×10^(4) Da to 1.87×10^(4) Da and altered the proportions of glucose,galactose and mannose,in which glucose increased from 45.94%to 48.16%.Methylation analysis and NMR spectra indicated that F-LEP-2a and NF-LEP-2a had similar linkage patterns.Furthermore,their immunomodulatory activities were evaluated with immunosuppressive mice.NF-LEP and F-LEP improved immune organ indices,immunoglobulin(Ig G and Ig M)and cytokines concentrations;restored the antioxidation capacity of liver;and maintained the balance of gut microbiota.F-LEP displayed better moderating effects on the spleen index,immunoglobulin,cytokines and the diversity of gut microbiota than NF-LEP(200,400 mg/kg).Our study provides an efficient and environment-friendly way for the structural modification of polysaccharides,which helps to enhance their biological activity and promote their wide application in food,medicine and other fields.
基金support provided by Thammasat University Research fund under the TSRI,Contract Nos.TUFF19/2564 and TUFF24/2565for the project of“AI Ready City Networking in RUN”,based on the RUN Digital Cluster collaboration scheme.This research project was also supported by the Thailand Science Research and Innovation fund,the University of Phayao(Grant No.FF65-RIM041)supported by National Science,Research and Innovation(NSRF),and King Mongkut’s University of Technology North Bangkok,Contract No.KMUTNB-FF-66-07.
文摘Accidents are still an issue in an intelligent transportation system,despite developments in self-driving technology(ITS).Drivers who engage in risky behavior account for more than half of all road accidents.As a result,reckless driving behaviour can cause congestion and delays.Computer vision and multimodal sensors have been used to study driving behaviour categorization to lessen this problem.Previous research has also collected and analyzed a wide range of data,including electroencephalography(EEG),electrooculography(EOG),and photographs of the driver’s face.On the other hand,driving a car is a complicated action that requires a wide range of body move-ments.In this work,we proposed a ResNet-SE model,an efficient deep learning classifier for driving activity clas-sification based on signal data obtained in real-world traffic conditions using smart glasses.End-to-end learning can be achieved by combining residual networks and channel attention approaches into a single learning model.Sensor data from 3-point EOG electrodes,tri-axial accelerometer,and tri-axial gyroscope from the Smart Glasses dataset was utilized in this study.We performed various experiments and compared the proposed model to base-line deep learning algorithms(CNNs and LSTMs)to demonstrate its performance.According to the research results,the proposed model outperforms the previous deep learning models in this domain with an accuracy of 99.17%and an F1-score of 98.96%.
文摘Lawsonia inermis is a hairless plant growing in various regions of North Africa, the Indian subcontinent, and the Middle East. It possesses many medicinal attributes, including curative properties against infectious dermatoses. This study was carried out to evaluate the phytochemical profile of the crude ethanolic extract of the plant leaves and its fractions as well as their antimicrobial activities. The phytochemical profile was performed using high-performance thin-layer chromatography (HPTLC), gas chromatography-mass spectrometry (GC-MS), and high-performance liquid chromatography (HPLC). Additionally, the phenolic and flavonoid contents were determined using the Folin-Ciocalteu spectrophotometric and the aluminum trichloride methods. Antimicrobial activity was tested using disc diffusion and microdilution methods. The presence of flavonoids, tannins, sterols, and triterpenes was revealed. GC-MS detected twelve compounds main compounds consisting of saturated and unsaturated fatty acids and phenolic and terpenoid compounds among twenty-seven components. HPLC also detected high contents of phenolic acids and flavonoids. The most abundant triterpene and sterols were ursolic acid (around 43.14 g/100g DW, 13.9 g/100g dry weight (DW), and 0.68 g/100g DW) in the crude ethanolic extract of leaves (FeLi), hexane fraction (FHLi) and dichloromethane fraction (FDLi), respectively and, β-sitosterol in FeLi (56.7 mg/100g DW), FHLi (10.55 g/100g DW), FDLi (106.1 mg/100g DW) and butanol fraction (FBLi) (357.4 mg/100g DW). Among the flavonoids, rutin = 3.24 g/100g and quercetin = 0.63 g/100g in the ethanolic extract, rutin = 15.73 g/100g in the dichloromethane fraction, and rutin = 0.23 g/100g) in the aqueous fraction;and among phenolic compounds, caffeic acid (37.65 g/100g DW) and vanillic acid (22.70 g/100g DW) were the most important in the ethyl acetate fraction (FAeLi). All organic fractions exhibited interesting antibacterial and antifungal activities against the tested strains, with the best activity recorded with the dichloromethane and ethyl acetate fractions. The leaf extracts’ phytochemical profile and antimicrobial activity support the use of Lawsonia inermis against infectious skin diseases.
