BACKGROUND Mesenchymal stem cells(MSCs)as living biopharmaceuticals with unique properties,i.e.,stemness,viability,phenotypes,paracrine activity,etc.,need to be administered such that they reach the target site,mainta...BACKGROUND Mesenchymal stem cells(MSCs)as living biopharmaceuticals with unique properties,i.e.,stemness,viability,phenotypes,paracrine activity,etc.,need to be administered such that they reach the target site,maintaining these properties unchanged and are retained at the injury site to participate in the repair process.Route of delivery(RoD)remains one of the critical determinants of safety and efficacy.This study elucidates the safety and effectiveness of different RoDs of MSC treatment in heart failure(HF)based on phase II randomized clinical trials(RCTs).We hypothesize that the RoD modulates the safety and efficacy of MSCbased therapy and determines the outcome of the intervention.AIM To investigate the effect of RoD of MSCs on safety and efficacy in HF patients.METHODS RCTs were retrieved from six databases.Safety endpoints included mortality and serious adverse events(SAEs),while efficacy outcomes encompassed changes in left ventricular ejection fraction(LVEF),6-minute walk distance(6MWD),and pro-B-type natriuretic peptide(pro-BNP).Subgroup analyses on RoD were performed for all study endpoints.RESULTS Twelve RCTs were included.Overall,MSC therapy demonstrated a significant decrease in mortality[relative risk(RR):0.55,95%confidence interval(95%CI):0.33-0.92,P=0.02]compared to control,while SAE outcomes showed no significant difference(RR:0.84,95%CI:0.66-1.05,P=0.11).RoD subgroup analysis revealed a significant difference in SAE among the transendocardial(TESI)injection subgroup(RR=0.71,95%CI:0.54-0.95,P=0.04).The pooled weighted mean difference(WMD)demonstrated an overall significant improvement of LVEF by 2.44%(WMD:2.44%,95%CI:0.80-4.29,P value≤0.001),with only intracoronary(IC)subgroup showing significant improvement(WMD:7.26%,95%CI:5.61-8.92,P≤0.001).Furthermore,the IC delivery route significantly improved 6MWD by 115 m(WMD=114.99 m,95%CI:91.48-138.50),respectively.In biochemical efficacy outcomes,only the IC subgroup showed a significant reduction in pro-BNP by-860.64 pg/mL(WMD:-860.64 pg/Ml,95%CI:-944.02 to-777.26,P=0.001).CONCLUSION Our study concluded that all delivery methods of MSC-based therapy are safe.Despite the overall benefits in efficacy,the TESI and IC routes provided better outcomes than other methods.Larger-scale trials are warranted before implementing MSC-based therapy in routine clinical practice.展开更多
More than two decades of in vitro experimentation supported by the data from experimental animal studies in both small as well as large experimental animal models have culminated into multiple clinical studies worldwi...More than two decades of in vitro experimentation supported by the data from experimental animal studies in both small as well as large experimental animal models have culminated into multiple clinical studies worldwide to assess their regenerative potential. Although the data generated from these studies have only met with cautious response from the researchers, efforts are still underway with the hope to refine the different aspects of cell-based therapy approach to develop it into an effective routine therapeutic intervention. Besides others, search for a cell type with optimal characteristics remains an area of intense research. Pluripotent stem cells in general, and induced pluripotent stem cells in particular have gained special attention of researchers due to their ability to adopt a morphofuntionally competent phenotype. They are being considered as surrogate embryonic stem cells albeit without moral and ethical issues of availability and having better immunological acceptability. We provide a head-to-head comparison of ESCs and iPSCs and an overview of stem cell therapy approach converging on the observed advantages of pluripotent stem cells during pre-clinical and clinical studies.展开更多
Diabetes is associated with many complications that could lead to death.Diabetic retinopathy,a complication of diabetes,is difficult to diagnose and may lead to vision loss.Visual identification of micro features in f...Diabetes is associated with many complications that could lead to death.Diabetic retinopathy,a complication of diabetes,is difficult to diagnose and may lead to vision loss.Visual identification of micro features in fundus images for the diagnosis of DR is a complex and challenging task for clinicians.Because clinical testing involves complex procedures and is timeconsuming,an automated system would help ophthalmologists to detect DR and administer treatment in a timelymanner so that blindness can be avoided.Previous research works have focused on image processing algorithms,or neural networks,or signal processing techniques alone to detect diabetic retinopathy.Therefore,we aimed to develop a novel integrated approach to increase the accuracy of detection.This approach utilized both convolutional neural networks and signal processing techniques.In this proposed method,the biological electro retinogram(ERG)sensor network(BSN)and deep convolution neural network(DCNN)were developed to detect and classify DR.In the BSN system,electrodes were used to record ERGsignal,which was preprocessed to be noise-free.Processing was performed in the frequency domain by the application of fast Fourier transform(FFT)and mel frequency cepstral coefficients(MFCCs)were extracted.Artificial neural network(ANN)classifier was used to classify the signals of eyes with DR and normal eye.Additionally,fundus images were captured using a fundus camera,and these were used as the input for DCNN-based analysis.The DCNN consisted of many layers to facilitate the extraction of features and classification of fundus images into normal images,non-proliferative DR(NPDR)or earlystage DR images,and proliferative DR(PDR)or advanced-stage DR images.Furthermore,it classifiedNPDRaccording tomicroaneurysms,hemorrhages,cotton wool spots,and exudates,and the presence of new blood vessels indicated PDR.The accuracy,sensitivity,and specificity of the ANNclassifier were found to be 94%,95%,and 93%,respectively.Both the accuracy rate and sensitivity rate of theDCNNclassifierwas 96.5%for the images acquired from various hospitals as well as databases.A comparison between the accuracy rates of BSN andDCNN approaches showed thatDCNNwith fundus images decreased the error rate to 4%.展开更多
The substantial vision loss due to Diabetic Retinopathy(DR)mainly damages the blood vessels of the retina.These feature changes in the blood vessels fail to exist any manifestation in the eye at its initial stage,if t...The substantial vision loss due to Diabetic Retinopathy(DR)mainly damages the blood vessels of the retina.These feature changes in the blood vessels fail to exist any manifestation in the eye at its initial stage,if this problem doesn’t exhibit initially,that leads to permanent blindness.So,this type of disorder can be only screened and identified through the processing of fundus images.The different stages in DR are Micro aneurysms(Ma),Hemorrhages(HE),and Exudates,and the stages in lesion show the chance of DR.For the advancement of early detection of DR in the eye we have developed the CNN-based identification approach on the fundus blood lesion image.The CNN-based automated detection of DR proposes the novel Graph cutter-built background and foreground superpixel segmentation technique and the foremost classification of fundus images feature was done through hybrid classifiers as K-Nearest Neighbor(KNN)classifier,Support Vector Machine(SVM)classifier,and Cascaded Rotation Forest(CRF)classifier.Over this classifier,the feature cross-validation made the classification more accurate and the comparison is made with the previous works of parameters such as specificity,sensitivity,and accuracy shows that the hybrid classifier attains excellent performance and achieves an overall accuracy of 98%.Among these Cascaded Rotation Forest(CRF)classifier has more accuracy than others.展开更多
Gestational Diabetes Mellitus(GDM)is an illness that represents a certain degree of glucose intolerance with onset or first recognition during pregnancy.In the past few decades,numerous investigations were conducted u...Gestational Diabetes Mellitus(GDM)is an illness that represents a certain degree of glucose intolerance with onset or first recognition during pregnancy.In the past few decades,numerous investigations were conducted upon early identification of GDM.Machine Learning(ML)methods are found to be efficient prediction techniques with significant advantage over statistical models.In this view,the current research paper presents an ensemble of ML-based GDM prediction and classification models.The presented model involves three steps such as preprocessing,classification,and ensemble voting process.At first,the input medical data is preprocessed in four levels namely,format conversion,class labeling,replacement of missing values,and normalization.Besides,four ML models such as Logistic Regression(LR),k-Nearest Neighbor(KNN),Support Vector Machine(SVM),and Random Forest(RF)are used for classification.In addition to the above,RF,LR,KNN and SVM classifiers are integrated to perform the final classification in which a voting classifier is also used.In order to investigate the proficiency of the proposed model,the authors conducted extensive set of simulations and the results were examined under distinct aspects.Particularly,the ensemble model has outperformed the classical ML models with a precision of 94%,recall of 94%,accuracy of 94.24%,and F-score of 94%.展开更多
Gestational Diabetes Mellitus(GDM)is one of the commonly occurring diseases among women during pregnancy.Oral Glucose Tolerance Test(OGTT)is followed universally in the diagnosis of GDM diagnosis at early pregnancy wh...Gestational Diabetes Mellitus(GDM)is one of the commonly occurring diseases among women during pregnancy.Oral Glucose Tolerance Test(OGTT)is followed universally in the diagnosis of GDM diagnosis at early pregnancy which is costly and ineffective.So,there is a need to design an effective and automated GDM diagnosis and classification model.The recent developments in the field of Deep Learning(DL)are useful in diagnosing different diseases.In this view,the current research article presents a new outlier detection with deep-stacked Autoencoder(OD-DSAE)model for GDM diagnosis and classification.The goal of the proposed OD-DSAE model is to find out those mothers with high risks and make them undergo earlier diagnosis,monitoring,and treatment compared to low-risk women.The presented ODDSAE model involves three major processes namely,preprocessing,outlier detection,and classification.In the first step i.e.,data preprocessing,there exists three stages namely,format conversion,class labelling,and missing value replacement using k-nearest neighbors(KNN)model.Outliers are superior values which considerably varies from other data observations.So,it might represent the variability in measurement,experimental errors or novelty too.So,Hierarchical Clustering(HC)-based outlier detection technique is incorporated in OD-DSAE model,and thereby classification performance can be improved.The proposed model was simulated using Python 3.6.5 on a dataset collected by the researcher themselves.A series of experiments was conducted and the results were investigated under different aspects.The experimental outcomes inferred that the OD-DSAE model has outperformed the compared methods and achieved high precision of 96.17%,recall of 98.69%,specificity of 89.50%,accuracy of 96.18%,and F-score of 97.41%.展开更多
The hypothesis of behavioral parameters dependence measured from person’s head movements in quasi-stationary state on COVID-19 disease is discussed. Method for determining the dependence of vestibular-emotional refle...The hypothesis of behavioral parameters dependence measured from person’s head movements in quasi-stationary state on COVID-19 disease is discussed. Method for determining the dependence of vestibular-emotional reflex parameters on COVID-19, various diseases and pathologies are proposed. Micro-movements of a head for representatives of the control group (with a confirmed absence of COVID-19 disease) and a group of patients with a confirmed diagnosis of COVID-19 were studied using vibraimage technology. Parameters and criteria for the diagnosis of COVID-19 for training artificial intelligence (AI) on the control group and the patient group are proposed. 3-layer (one hidden layer) feedforward neural network (40 + 20 + 1 sigmoid neurons) was developed for AI training. AI was firstly trained on the primary sample of patients and a control group. Study of a random sample of people with trained AI was carried out and the possibility of detecting COVID-19 using the proposed method was proved a week before the onset of clinical symptoms of the disease. Number of COVID-19 diagnostic parameters was increased to 26 and AI was trained on a sample of 536 measurements, 268 patient measurement results and 268 measurement results in the control group. The achieved diagnostic accuracy was more than 99%, 4 errors per 536 measurements (2 false positive and 2 false negative), specificity 99.25% and sensitivity 99.25%. The issues of improving the accuracy and reliability of the proposed method for diagnosing COVID-19 are discussed. Further ways to improve the characteristics and applicability of the proposed method of diagnosis and self-diagnosis of COVID-19 are outlined.展开更多
The article is the result of theoretical and experimental studies aimed at determining the structural groups of modern bituminous materials in order to assess the raw materials, production technology, rational directi...The article is the result of theoretical and experimental studies aimed at determining the structural groups of modern bituminous materials in order to assess the raw materials, production technology, rational directions for their use in construction, the road industry and waterproofing. Commercial oil bitumen, raw tars and heavy oil residues (semi-finished products) of oil refineries aimed at meeting large-tonnage needs have been studied. The assessment was carried out according to the group hydrocarbon composition, by liquid chromatography using model compounds. Comparative analysis showed a general trend for all studied samples of petroleum bitumen: low content of asphaltenes (from 3.9 to 23.9 wt.%), low content of resins (from 11 to 19.07 wt%), insufficient for the formation of stable structuring layers, and a significant content of aromatic hydrocarbons, including heavy aromatic compounds (more than 20 wt.%). An assumption was made about the influence of the origin and the structure obtained during the processing of asphaltenes and resins on the transition from one type of bituminous structure to another based on the lyophility of high-molecular group components. A comparative structural characteristic of heavy oil residues from gasoline and oil production is considered in comparison with bitumens of various viscosities. Recommendations are given on the technology of processing petroleum feedstock and the use of heavy oils in order to obtain a given bitumen structure for the production of rational bitumen products for construction and waterproofing.展开更多
Non-steroidal anti-inflammatory drugs’anti-pyretic and anti-inflammatory effects has led some individuals to theorize these medications may blunt core body temperature(Tc)increases during exercise.We utilized a doubl...Non-steroidal anti-inflammatory drugs’anti-pyretic and anti-inflammatory effects has led some individuals to theorize these medications may blunt core body temperature(Tc)increases during exercise.We utilized a double-blind,randomized,and counterbalanced cross-over design to examine the effects of a 24-h naproxen dose(3–220 mg naproxen pills)and placebo(0 mg naproxen)on Tc and plasma interleukin-6(IL-6)concentrations during cycling in a hot or ambient environment.Participants(n=11;6 male,5 female;age=27.8±6.5 years,weight=79.1±17.9 kg,height=177±9.5 cm)completed 4 conditions:1)placebo and ambient(Control);2)placebo and heat(Heat);3)naproxen and ambient(Npx);and 4)naproxen and heat(NpxHeat).Dependent measures were taken before,during,and immediately after 90 min of cycling and then 3 h after cycling.Overall,Tc significantly increased pre-(37.1±0.4℃)to post-cycling(38.2±0.3℃,F_(1.7,67.3)=150.5,p<0.001)and decreased during rest(37.0±0.3℃,F_(2.0,81.5)=201.6,p<0.001).Rate of change or maximum Tc were not significantly different between conditions.IL-6 increased pre-(0.54±0.06 pg/ml)to post-exercise(2.46±0.28 pg/ml,p<0.001)and remained significantly higher than pre-at 3 h post-(1.17±0.14 pg/ml,95%CI=-1.01 to-0.23,p=0.001).No significant IL-6 differences occurred between conditions.A 24-h,over-the-counter naproxen dose did not significantly affect Tc or IL-6 among males and females cycling in hot or ambient environments.展开更多
文摘BACKGROUND Mesenchymal stem cells(MSCs)as living biopharmaceuticals with unique properties,i.e.,stemness,viability,phenotypes,paracrine activity,etc.,need to be administered such that they reach the target site,maintaining these properties unchanged and are retained at the injury site to participate in the repair process.Route of delivery(RoD)remains one of the critical determinants of safety and efficacy.This study elucidates the safety and effectiveness of different RoDs of MSC treatment in heart failure(HF)based on phase II randomized clinical trials(RCTs).We hypothesize that the RoD modulates the safety and efficacy of MSCbased therapy and determines the outcome of the intervention.AIM To investigate the effect of RoD of MSCs on safety and efficacy in HF patients.METHODS RCTs were retrieved from six databases.Safety endpoints included mortality and serious adverse events(SAEs),while efficacy outcomes encompassed changes in left ventricular ejection fraction(LVEF),6-minute walk distance(6MWD),and pro-B-type natriuretic peptide(pro-BNP).Subgroup analyses on RoD were performed for all study endpoints.RESULTS Twelve RCTs were included.Overall,MSC therapy demonstrated a significant decrease in mortality[relative risk(RR):0.55,95%confidence interval(95%CI):0.33-0.92,P=0.02]compared to control,while SAE outcomes showed no significant difference(RR:0.84,95%CI:0.66-1.05,P=0.11).RoD subgroup analysis revealed a significant difference in SAE among the transendocardial(TESI)injection subgroup(RR=0.71,95%CI:0.54-0.95,P=0.04).The pooled weighted mean difference(WMD)demonstrated an overall significant improvement of LVEF by 2.44%(WMD:2.44%,95%CI:0.80-4.29,P value≤0.001),with only intracoronary(IC)subgroup showing significant improvement(WMD:7.26%,95%CI:5.61-8.92,P≤0.001).Furthermore,the IC delivery route significantly improved 6MWD by 115 m(WMD=114.99 m,95%CI:91.48-138.50),respectively.In biochemical efficacy outcomes,only the IC subgroup showed a significant reduction in pro-BNP by-860.64 pg/mL(WMD:-860.64 pg/Ml,95%CI:-944.02 to-777.26,P=0.001).CONCLUSION Our study concluded that all delivery methods of MSC-based therapy are safe.Despite the overall benefits in efficacy,the TESI and IC routes provided better outcomes than other methods.Larger-scale trials are warranted before implementing MSC-based therapy in routine clinical practice.
文摘More than two decades of in vitro experimentation supported by the data from experimental animal studies in both small as well as large experimental animal models have culminated into multiple clinical studies worldwide to assess their regenerative potential. Although the data generated from these studies have only met with cautious response from the researchers, efforts are still underway with the hope to refine the different aspects of cell-based therapy approach to develop it into an effective routine therapeutic intervention. Besides others, search for a cell type with optimal characteristics remains an area of intense research. Pluripotent stem cells in general, and induced pluripotent stem cells in particular have gained special attention of researchers due to their ability to adopt a morphofuntionally competent phenotype. They are being considered as surrogate embryonic stem cells albeit without moral and ethical issues of availability and having better immunological acceptability. We provide a head-to-head comparison of ESCs and iPSCs and an overview of stem cell therapy approach converging on the observed advantages of pluripotent stem cells during pre-clinical and clinical studies.
文摘Diabetes is associated with many complications that could lead to death.Diabetic retinopathy,a complication of diabetes,is difficult to diagnose and may lead to vision loss.Visual identification of micro features in fundus images for the diagnosis of DR is a complex and challenging task for clinicians.Because clinical testing involves complex procedures and is timeconsuming,an automated system would help ophthalmologists to detect DR and administer treatment in a timelymanner so that blindness can be avoided.Previous research works have focused on image processing algorithms,or neural networks,or signal processing techniques alone to detect diabetic retinopathy.Therefore,we aimed to develop a novel integrated approach to increase the accuracy of detection.This approach utilized both convolutional neural networks and signal processing techniques.In this proposed method,the biological electro retinogram(ERG)sensor network(BSN)and deep convolution neural network(DCNN)were developed to detect and classify DR.In the BSN system,electrodes were used to record ERGsignal,which was preprocessed to be noise-free.Processing was performed in the frequency domain by the application of fast Fourier transform(FFT)and mel frequency cepstral coefficients(MFCCs)were extracted.Artificial neural network(ANN)classifier was used to classify the signals of eyes with DR and normal eye.Additionally,fundus images were captured using a fundus camera,and these were used as the input for DCNN-based analysis.The DCNN consisted of many layers to facilitate the extraction of features and classification of fundus images into normal images,non-proliferative DR(NPDR)or earlystage DR images,and proliferative DR(PDR)or advanced-stage DR images.Furthermore,it classifiedNPDRaccording tomicroaneurysms,hemorrhages,cotton wool spots,and exudates,and the presence of new blood vessels indicated PDR.The accuracy,sensitivity,and specificity of the ANNclassifier were found to be 94%,95%,and 93%,respectively.Both the accuracy rate and sensitivity rate of theDCNNclassifierwas 96.5%for the images acquired from various hospitals as well as databases.A comparison between the accuracy rates of BSN andDCNN approaches showed thatDCNNwith fundus images decreased the error rate to 4%.
文摘The substantial vision loss due to Diabetic Retinopathy(DR)mainly damages the blood vessels of the retina.These feature changes in the blood vessels fail to exist any manifestation in the eye at its initial stage,if this problem doesn’t exhibit initially,that leads to permanent blindness.So,this type of disorder can be only screened and identified through the processing of fundus images.The different stages in DR are Micro aneurysms(Ma),Hemorrhages(HE),and Exudates,and the stages in lesion show the chance of DR.For the advancement of early detection of DR in the eye we have developed the CNN-based identification approach on the fundus blood lesion image.The CNN-based automated detection of DR proposes the novel Graph cutter-built background and foreground superpixel segmentation technique and the foremost classification of fundus images feature was done through hybrid classifiers as K-Nearest Neighbor(KNN)classifier,Support Vector Machine(SVM)classifier,and Cascaded Rotation Forest(CRF)classifier.Over this classifier,the feature cross-validation made the classification more accurate and the comparison is made with the previous works of parameters such as specificity,sensitivity,and accuracy shows that the hybrid classifier attains excellent performance and achieves an overall accuracy of 98%.Among these Cascaded Rotation Forest(CRF)classifier has more accuracy than others.
文摘Gestational Diabetes Mellitus(GDM)is an illness that represents a certain degree of glucose intolerance with onset or first recognition during pregnancy.In the past few decades,numerous investigations were conducted upon early identification of GDM.Machine Learning(ML)methods are found to be efficient prediction techniques with significant advantage over statistical models.In this view,the current research paper presents an ensemble of ML-based GDM prediction and classification models.The presented model involves three steps such as preprocessing,classification,and ensemble voting process.At first,the input medical data is preprocessed in four levels namely,format conversion,class labeling,replacement of missing values,and normalization.Besides,four ML models such as Logistic Regression(LR),k-Nearest Neighbor(KNN),Support Vector Machine(SVM),and Random Forest(RF)are used for classification.In addition to the above,RF,LR,KNN and SVM classifiers are integrated to perform the final classification in which a voting classifier is also used.In order to investigate the proficiency of the proposed model,the authors conducted extensive set of simulations and the results were examined under distinct aspects.Particularly,the ensemble model has outperformed the classical ML models with a precision of 94%,recall of 94%,accuracy of 94.24%,and F-score of 94%.
基金The authors received no specific funding for this study。
文摘Gestational Diabetes Mellitus(GDM)is one of the commonly occurring diseases among women during pregnancy.Oral Glucose Tolerance Test(OGTT)is followed universally in the diagnosis of GDM diagnosis at early pregnancy which is costly and ineffective.So,there is a need to design an effective and automated GDM diagnosis and classification model.The recent developments in the field of Deep Learning(DL)are useful in diagnosing different diseases.In this view,the current research article presents a new outlier detection with deep-stacked Autoencoder(OD-DSAE)model for GDM diagnosis and classification.The goal of the proposed OD-DSAE model is to find out those mothers with high risks and make them undergo earlier diagnosis,monitoring,and treatment compared to low-risk women.The presented ODDSAE model involves three major processes namely,preprocessing,outlier detection,and classification.In the first step i.e.,data preprocessing,there exists three stages namely,format conversion,class labelling,and missing value replacement using k-nearest neighbors(KNN)model.Outliers are superior values which considerably varies from other data observations.So,it might represent the variability in measurement,experimental errors or novelty too.So,Hierarchical Clustering(HC)-based outlier detection technique is incorporated in OD-DSAE model,and thereby classification performance can be improved.The proposed model was simulated using Python 3.6.5 on a dataset collected by the researcher themselves.A series of experiments was conducted and the results were investigated under different aspects.The experimental outcomes inferred that the OD-DSAE model has outperformed the compared methods and achieved high precision of 96.17%,recall of 98.69%,specificity of 89.50%,accuracy of 96.18%,and F-score of 97.41%.
文摘The hypothesis of behavioral parameters dependence measured from person’s head movements in quasi-stationary state on COVID-19 disease is discussed. Method for determining the dependence of vestibular-emotional reflex parameters on COVID-19, various diseases and pathologies are proposed. Micro-movements of a head for representatives of the control group (with a confirmed absence of COVID-19 disease) and a group of patients with a confirmed diagnosis of COVID-19 were studied using vibraimage technology. Parameters and criteria for the diagnosis of COVID-19 for training artificial intelligence (AI) on the control group and the patient group are proposed. 3-layer (one hidden layer) feedforward neural network (40 + 20 + 1 sigmoid neurons) was developed for AI training. AI was firstly trained on the primary sample of patients and a control group. Study of a random sample of people with trained AI was carried out and the possibility of detecting COVID-19 using the proposed method was proved a week before the onset of clinical symptoms of the disease. Number of COVID-19 diagnostic parameters was increased to 26 and AI was trained on a sample of 536 measurements, 268 patient measurement results and 268 measurement results in the control group. The achieved diagnostic accuracy was more than 99%, 4 errors per 536 measurements (2 false positive and 2 false negative), specificity 99.25% and sensitivity 99.25%. The issues of improving the accuracy and reliability of the proposed method for diagnosing COVID-19 are discussed. Further ways to improve the characteristics and applicability of the proposed method of diagnosis and self-diagnosis of COVID-19 are outlined.
文摘The article is the result of theoretical and experimental studies aimed at determining the structural groups of modern bituminous materials in order to assess the raw materials, production technology, rational directions for their use in construction, the road industry and waterproofing. Commercial oil bitumen, raw tars and heavy oil residues (semi-finished products) of oil refineries aimed at meeting large-tonnage needs have been studied. The assessment was carried out according to the group hydrocarbon composition, by liquid chromatography using model compounds. Comparative analysis showed a general trend for all studied samples of petroleum bitumen: low content of asphaltenes (from 3.9 to 23.9 wt.%), low content of resins (from 11 to 19.07 wt%), insufficient for the formation of stable structuring layers, and a significant content of aromatic hydrocarbons, including heavy aromatic compounds (more than 20 wt.%). An assumption was made about the influence of the origin and the structure obtained during the processing of asphaltenes and resins on the transition from one type of bituminous structure to another based on the lyophility of high-molecular group components. A comparative structural characteristic of heavy oil residues from gasoline and oil production is considered in comparison with bitumens of various viscosities. Recommendations are given on the technology of processing petroleum feedstock and the use of heavy oils in order to obtain a given bitumen structure for the production of rational bitumen products for construction and waterproofing.
文摘Non-steroidal anti-inflammatory drugs’anti-pyretic and anti-inflammatory effects has led some individuals to theorize these medications may blunt core body temperature(Tc)increases during exercise.We utilized a double-blind,randomized,and counterbalanced cross-over design to examine the effects of a 24-h naproxen dose(3–220 mg naproxen pills)and placebo(0 mg naproxen)on Tc and plasma interleukin-6(IL-6)concentrations during cycling in a hot or ambient environment.Participants(n=11;6 male,5 female;age=27.8±6.5 years,weight=79.1±17.9 kg,height=177±9.5 cm)completed 4 conditions:1)placebo and ambient(Control);2)placebo and heat(Heat);3)naproxen and ambient(Npx);and 4)naproxen and heat(NpxHeat).Dependent measures were taken before,during,and immediately after 90 min of cycling and then 3 h after cycling.Overall,Tc significantly increased pre-(37.1±0.4℃)to post-cycling(38.2±0.3℃,F_(1.7,67.3)=150.5,p<0.001)and decreased during rest(37.0±0.3℃,F_(2.0,81.5)=201.6,p<0.001).Rate of change or maximum Tc were not significantly different between conditions.IL-6 increased pre-(0.54±0.06 pg/ml)to post-exercise(2.46±0.28 pg/ml,p<0.001)and remained significantly higher than pre-at 3 h post-(1.17±0.14 pg/ml,95%CI=-1.01 to-0.23,p=0.001).No significant IL-6 differences occurred between conditions.A 24-h,over-the-counter naproxen dose did not significantly affect Tc or IL-6 among males and females cycling in hot or ambient environments.
