The project discusses the development of a deep learning model to detect osteoporosis from dental panoramic X-Ray images. It provides an in-depth understanding of human bone structure, osteoporosis, its symptoms, caus...The project discusses the development of a deep learning model to detect osteoporosis from dental panoramic X-Ray images. It provides an in-depth understanding of human bone structure, osteoporosis, its symptoms, causes, prevalence, and risk factors. The project also explains bone density measurement using dual-energy X-ray absorptiometry (DEXA) and the application of artificial intelligence (AI) and machine learning (ML) in medical imaging. The study uses panoramic dental X-rays to evaluate AI technology in dental imaging and classification of mandible inferior cortical based on Klemetti and Kolmakow criteria. The model architecture consists of convolutional, pooling, fully connected, ReLU, and Softmax layers. Dropout and early stopping are added to the model. The training process uses the train-test approach with 100 epochs and a batch size of 32, and performance evaluation measures such as accuracy, sensitivity, specificity, and F1-score are used to assess the classifier’s performance. The findings and methodology provide a comprehensive understanding of the application of deep learning in the detection of osteoporosis from dental panoramic X-Ray images, and the study demonstrates a robust approach to implementing AI in medical imaging for osteoporosis detection.展开更多
Computed tomography plays an important role in industrial non-destructive testing, medical applications, astronomy and many other fields to look inside the scanned object and to analysis its inner structures. A non-de...Computed tomography plays an important role in industrial non-destructive testing, medical applications, astronomy and many other fields to look inside the scanned object and to analysis its inner structures. A non-destructive testing software have been developed to efficiently detect inner flaws of space industrial components. As the core of our software, reconstruction algorithms including preprocess of raw data, re-arrange algorithm and filtered back-projection algorithms have been described in detail in this article. With real raw data from CASC of China, experimental results verified the applied reconstruction algorithm in our software. Furthermore, forward algorithms simulating generation of fan-beam raw data are also presented in this article.展开更多
Epithelial and stromal tissues are components of the tumor microenvironment and play a major role in tumor initiation and progression.Distinguishing stroma from epithelial tissues is critically important for spatial c...Epithelial and stromal tissues are components of the tumor microenvironment and play a major role in tumor initiation and progression.Distinguishing stroma from epithelial tissues is critically important for spatial characterization of the tumor microenvironment.Here,we propose BrcaSeg,an image analysis pipeline based on a convolutional neural network(CNN)model to classify epithelial and stromal regions in whole-slide hematoxylin and eosin(H&E)stained histopathological images.The CNN model is trained using well-annotated breast cancer tissue microarrays and validated with images from The Cancer Genome Atlas(TCGA)Program.BrcaSeg achieves a classification accuracy of 91.02%,which outperforms other state-of-the-art methods.Using this model,we generate pixel-level epithelial/stromal tissue maps for 1000 TCGA breast cancer slide images that are paired with gene expression data.We subsequently estimate the epithelial and stromal ratios and perform correlation analysis to model the relationship between gene expression and tissue ratios.Gene Ontology(GO)enrichment analyses of genes that are highly correlated with tissue ratios suggest that the same tissue is associated with similar biological processes in different breast cancer subtypes,whereas each subtype also has its own idiosyncratic biological processes governing the development of these tissues.Taken all together,our approach can lead to new insights in exploring relationships between image-based phenotypes and their underlying genomic events and biological processes for all types of solid tumors.BrcaSeg can be accessed at https://github.com/Serian1992/ImgBio.展开更多
Background: The zone of calcified cartilage (ZCC) plays an important role in the pathogenesis of osteoarthritis (OA) but has never been imaged in vivo with magnetic resonance (MR) imaging techniques. We investigated t...Background: The zone of calcified cartilage (ZCC) plays an important role in the pathogenesis of osteoarthritis (OA) but has never been imaged in vivo with magnetic resonance (MR) imaging techniques. We investigated the feasibility of direct imaging of the ZCC in both cadaveric whole knee specimens and in vivo healthy knees using a 3-dimensional ultrashort echo time cones (3D UTE-Cones) sequence on a clinical 3T scanner. Methods: In all, 12 cadaveric knee joints and 10 in vivo healthy were collected. At a 3T MR scanner with an 8-channel knee coil, a fat-saturated 3D dual-echo UTE-Cones sequence was used to image the ZCC, following with a short rectangular pulse excitation and 3D spiral sampling with conical view ordering. The regions of interests (ROIs) were delineated by a blinded observer. Singlecomponent T2* and T2 values were calculated from fat-saturated 3D dual-echo UTE-Cones and a Carr-Purcell-Meiboom-Gill (T2 CPMG) data using a semi-automated MATLAB code. Results: The single-exponential fitting curve of ZCC was accurately obtained with R2 of 0.989. For keen joint samples, the ZCC has a short T2* ranging from 0.62 to 2.55 ms, with the mean ±standard deviation (SD) of 1.49 ±0.66 ms, and with 95% confidence intervals (CI) of 1.20-1.78 ms. For volunteers, the short T2* ranges from 0.93 to 3.52ms, with the mean±SD of 2.09±0.56 ms, and the 95% CI is 1.43 to 2.74ms in ZCC. Conclusions: The high-resolution 3D UTE-Cones sequence might be used to directly image ZCC in the human knee joint on a clinical 3T scanner with a scan time of more than 10 min. Using this non-invasive technique, the T2* relaxation time of the ZCC can be further detected.展开更多
Conductive hydrogels are good candidates for flexible wearable sensors, which have received considerable attention for use in human-machine interfaces, human motion/health monitoring, and soft robots. However, these h...Conductive hydrogels are good candidates for flexible wearable sensors, which have received considerable attention for use in human-machine interfaces, human motion/health monitoring, and soft robots. However, these hydrogels often freeze at low temperatures and thus, exhibit low transparency, weak mechanical strength and stretchability, as well as poor adhesion strength.In this paper, conductive organohydrogels were prepared by thermal polymerization of acrylamide and N-(3-aminopropyl)methacrylamide in a glycerol-water binary solvent using Na Cl as a conductive salt. Compared to other organohydrogels, our organohydrogels featured higher fracture stress(170 kPa) and greater stretchability(900%). The organohydrogels showed excellent antifreezing properties and high transparency(97%, at 400–800 nm wavelength) and presented outstanding adhesion strength to a variety of substrates. The conductive organohydrogels that were stored at -20℃ for 24 h could still respond to both strain and pressure, showing a high sensitivity(gauge factor=2.73 under 100% strain), fast response time(0.4 s), and signal repeatability during multiple force cycles(~100 cycles). Furthermore, the conductivity of cleaved antifreezing gels could be restored by contacting the broken surfaces together. Finally, we used our organohydrogels to monitor human tremors and bradykinesia in real-time within wired and wireless models, thus presenting a potential application for Parkinson’s disease diagnosis.展开更多
We present a sparse Bayesian reconstruction method based on multiple types of a priori information for multispectral bioluminescence tomography （BLT）. In the Bayesian approach, five kinds of a priori information are...We present a sparse Bayesian reconstruction method based on multiple types of a priori information for multispectral bioluminescence tomography （BLT）. In the Bayesian approach, five kinds of a priori information are incorporated, reducing the ill-posedness of BLT. Specifically, source sparsity characteristic is considered to promote reconstruction results. Considering the computational burden in the multispectral case, a series of strategies is adopted to improve computational efficiency, such as optimal permissible source region strategy and node model of the finite element method. The performance of the proposed algorithm is validated by a heterogeneous three-dimensional （3D） micron scale computed tomography atlas and a mouse-shaped phantom. Reconstructed results demonstrate the feasibility and effectiveness of the proposed algorithm.展开更多
Evaluating bone regularly is important to prevent and control the disease of osteoporosis. Impact of osteoporosis on ultrasonic guided waves propagating in human long bones is studied in this paper. Multi-scale wavele...Evaluating bone regularly is important to prevent and control the disease of osteoporosis. Impact of osteoporosis on ultrasonic guided waves propagating in human long bones is studied in this paper. Multi-scale wavelet transform is proposed to process the received guided waves, and by analyzing energy changes in detail components of high order wavelet at different propagating distance to assess if osteoporosis happened. The guided waves signals are collected from the tibias of 13 volunteers. Based on the analysis of multi-scale wavelet transform, the high order detail components d6 and d5 changed dramatically with the propagation of ultrasonic guided waves along long bones, which means these 7 volunteers are diagnosed with osteoporosis. Compared with X-ray diagnosis, the effectiveness of this method can reach 92.3% in 13 volunteers. This suggests the multi-scale wavelet transform method is potential in ultrasonic assessment of bone quality.展开更多
Background Precise estimation of current and future comorbidities of patients with cardiovascular disease is an important factor in prioritizing continuous physiological monitoring and new therapies.Machine learning(M...Background Precise estimation of current and future comorbidities of patients with cardiovascular disease is an important factor in prioritizing continuous physiological monitoring and new therapies.Machine learning(ML)models have shown satisfactory performance in short-term mortality prediction in patients with heart disease,whereas their utility in long-term predictions is limited.This study aimed to investigate the performance of tree-based ML models on long-term mortality prediction and effect of two recently introduced biomarkers on long-term mortality.Methods This study used publicly available data from the Collaboration Center of Health Information Appli-cation at the Ministry of Health and Welfare,Taiwan,China.The collected data were from patients admitted to the cardiac care unit for acute myocardial infarction(AMI)between November 2003 and September 2004.We collected and analyzed mortality data up to December 2018.Medical records were used to gather demo-graphic and clinical data,including age,gender,body mass index,percutaneous coronary intervention status,and comorbidities such as hypertension,dyslipidemia,ST-segment elevation myocardial infarction,and non-ST-segment elevation myocardial infarction.Using the data,collected from 139 patients with AMI,from medical and demographic records as well as two recently introduced biomarkers,brachial pre-ejection period(bPEP)and brachial ejection time(bET),we investigated the performance of advanced ensemble tree-based ML algorithms(random forest,AdaBoost,and XGBoost)to predict all-cause mortality within 14 years.A nested cross-validation was performed to evaluate and compare the performance of our developed models precisely with that of the conventional logistic regression(LR)as the baseline method.Results The developed ML models achieved significantly better performance compared to the baseline LR(C-Statistic,0.80 for random forest,0.79 for AdaBoost,and 0.78 for XGBoost,vs.0.77 for LR)(PRF<0.001,PAdaBoost<0.001,and PXGBoost<0.05).Adding bPEP and bET to our feature set significantly improved the performance of the algorithm,leading to an absolute increase in C-statistic of up to 0.03(C-statistic,0.83 for random forest,0.82 for AdaBoost,and 0.80 for XGBoost,vs.0.74 for LR)(PRF<0.001,PAdaBoost<0.001,PXGBoost<0.05).Conclusion The study indicates that incorporating new biomarkers into advanced ML models may significantly improve long-term mortality prediction in patients with cardiovascular diseases.This advancement may enable better treatment prioritization for high-risk individuals.展开更多
In the application of cancellous bone ultrasound diagnosis based on backscattering method, it is of great importance to estimate fast and accurately whether the valid backscattering signal exists in the received signa...In the application of cancellous bone ultrasound diagnosis based on backscattering method, it is of great importance to estimate fast and accurately whether the valid backscattering signal exists in the received signal. We propose a fast estimation method based on spectrum entropy method. With 984 records of adult calcaneus clinical data, we estimate the validity of the backscatter signal using this method. The results of the proposed method and the results of experience-base judgement were compared and analyzed. And two key parameters, the signal range length and the segment number of the spectrum entropy, were analyzed. The results show when the signal range length is 13 I^s and the segment number is 15 20, this method can get the best result （accuracy〉95%, sensitivity〉99%, specificity〉87%）, while taking little calculation time （1.5 ms）. Therefore, this spectrum entropy method can satisfy the accuracy and real-time requirements in the ultrasonic estimation for cancellous bone.展开更多
In this paper,we propose a simple organohydrogel based capacitive humidity sensor for noncontact artificial sensation applications.The sensor is simple in design and consists of a transparent polyacrylamide organohydr...In this paper,we propose a simple organohydrogel based capacitive humidity sensor for noncontact artificial sensation applications.The sensor is simple in design and consists of a transparent polyacrylamide organohydrogel thin film attached on a flexible inter-digit electrode layer.The process of water absorption and desorption is reversible,thus the dielectric of the organohydrogel film as well as the overall capacitance is dependent on environmental humidity.The water absorption capacity and structural reliability of the device have been largely improved by adding glycerol in the organohydrogel network.By optimizing both the glycerol concentration and organohydrogel film thickness,the sensor can respond to cyclic humidity changes in a period of 300 ms.In addition,this sensor achieves a high relative capacitance increase(by 20 folds)in a wide relative humidity range(12%-95%).The sensor also exhibits high stability under different bending curvatures(up to 6.81 mm),wide temperature changes(20℃-40℃)and external pressures(0-8 N).To demonstrate the applications in wearable electronics,we found that the sensor was successful in detecting respiration intensity and rate as well as the difference in moisture content in various objects,i.e.,human skin and leaf surface.This sensor is highly sensitive and can be useful in the detection of the widerange of humidity changes.展开更多
To the Editor:Adipose tissue occurs in at least two different entities in mammals and humans:brown adipose tissue(BAT)and white adipose tissue(WAT).BAT is characterized by a unique uncoupling protein 1(UCP1)in the mit...To the Editor:Adipose tissue occurs in at least two different entities in mammals and humans:brown adipose tissue(BAT)and white adipose tissue(WAT).BAT is characterized by a unique uncoupling protein 1(UCP1)in the mitochondria that enables the uncoupling of the respiratory chain from adenosine triphosphate synthesis.Thus,energy is dissipated as heat to reduce fat accumulation.BAT is also considered a highly heterogeneous tissue with abundant oxygen,blood supply,and iron-rich mitochondria.[1,2]Activation of BAT via exposure to a cold environment is considered to be a means of reducing triglycerides to fight obesity.[3]The alterations in cells and tissues of activated BAT include increased iron content and UCP1 expression in mitochondrial,blood perfusion,and lipid utilization.[4]Therefore,accurate identification and quantitative analysis of inactive and activated BAT are of great significance for the treatment of metabolic diseases that target BAT,such as obesity.展开更多
With the rapid development of functional magnetic resonance imaging (fMRI) technology, the spatial resolution of fMRI data is continuously growing. This pro- vides us the possibility to detect the fine-scale patterns ...With the rapid development of functional magnetic resonance imaging (fMRI) technology, the spatial resolution of fMRI data is continuously growing. This pro- vides us the possibility to detect the fine-scale patterns of brain activities. The es- tablished univariate and multivariate methods to analyze fMRI data mostly focus on detecting the activation blobs without considering the distributed fine-scale pat- terns within the blobs. To improve the sensitivity of the activation detection, in this paper, multivariate statistical method and univariate statistical method are com- bined to discover the fine-grained activity patterns. For one voxel in the brain, a local homogenous region is constructed. Then, time courses from the local ho- mogenous region are integrated with multivariate statistical method. Univariate statistical method is finally used to construct the interests of statistic for that voxel. The approach has explicitly taken into account the structures of both activity pat- terns and existing noise of local brain regions. Therefore, it could highlight the fine-scale activity patterns of the local regions. Experiments with simulated and real fMRI data demonstrate that the proposed method dramatically increases the sensitivity of detection of fine-scale brain activity patterns which contain the subtle information about experimental conditions.展开更多
To avoid the ill-posedness in the inverse problem of bioluminescence tomography, a moment searching algorithm fusing the finite element method （FEM） with the moment concept in theoretical mechanics is developed. In ...To avoid the ill-posedness in the inverse problem of bioluminescence tomography, a moment searching algorithm fusing the finite element method （FEM） with the moment concept in theoretical mechanics is developed. In the algorithm, the source＇s information is mapped to the surface photon flux density by FEM, and the source＇s position is modified with the feedback through the algorithm of barycenter searching, which makes full use of the position information of the photon flux density on surface. The position is modified in every iterative step and will finally converge to the real source＇s value theoretically.展开更多
Radiotherapy(RT)mediated tumor immunogenicity offers an opportunity for simultaneous RT and immunotherapy via immunogenic cell death(ICD),which releases damaged-associated molecular patterns and generates“eat me”sig...Radiotherapy(RT)mediated tumor immunogenicity offers an opportunity for simultaneous RT and immunotherapy via immunogenic cell death(ICD),which releases damaged-associated molecular patterns and generates“eat me”signals for the innate immune system to modulate the immunogenicity.However,tumor hypoxia significantly reduces the therapeutic efficacy of RT and hampers its mediation of ICD induction.Herein,Au@Bi_(2)Te_(3)-polyethylene glycol(PEG)was rationally constructed as theranostic nanozymes for mild photothermal therapy,tumor hypoxia modulation,and RT adjuvant cancer immunotherapy.The tumor-specific production of oxygen could not only augment the effects of RT by enhanced reactive oxygen species(ROS)generation,but also reduce hypoxia-related cytokines and downregulate programmed cell death-ligand 1(PD-L1)to unleash immune-enhancing T cells.Moreover,Au@Bi_(2)Te_(3)-PEG could act as an immune-blocking inhibitor by efficient ICD induction with the combination of mild-photothermal therapy+RT to inhibit the tumor immune escape and improve antitumor immune response.Increased amounts of CD^(4+)and CD^(8+)Tcells and elevated levels of cytokines could be observed that eventually led to effective post-medication inhibition of primary and abscopal tumors.Spectral computed tomography/photoacoustic imaging allowed noninvasive and real-time tracking of nanoparticle(NP)accumulation and oxygenation status at tumor sites.Collectively,Au@Bi_(2)Te_(3)-PEG NPs could serve as effective theranostic nanoregulators with remarkable synergistic mildphotothermal/RT/immunotherapy effects that helped reshape the immune microenvironment and had remarkable molecular imaging properties.展开更多
Glutathione(GSH)is an important biological thiol in cells,which is involved in many physiological processes in the organism and regulates pathological processes of cells.Rapid and accurate monitoring of GSH in vitro a...Glutathione(GSH)is an important biological thiol in cells,which is involved in many physiological processes in the organism and regulates pathological processes of cells.Rapid and accurate monitoring of GSH in vitro and in vivo is quite needed in investigating important biochemical events.In this contribution,innovative cerium(Ce)doped polyaniline(Ce–Fe@PANI NPs)were prepared via Fe(III)induced oxidization polymerization method.Upon addition of GSH,the absorption of Ce–Fe@PANI NPs red shifted from the visible to the NIR region,con-firming the excellent absorption response to GSH.Moreover,Ce–Fe@PANI NPs exhibited excellent photoacoustic(PA)imaging enhancement in tube and shifted the PA intensity peak from 680 nm to 820 nm upon addition of GSH.In vitro and in vivo experiment verified that Ce–Fe@PANI NPs can monitor GSH in deep tissues via PA imaging technology.Collectively,this research provides Ce–Fe@PANI NPs would serve as a powerful nano-platform to realize PA imaging detection of GSH in vitro and in vivo.展开更多
This study aims to evaluate the therapeutic effects of laser acupuncture(LA)on articular cartilage degradation at the early stage of postmenopausal osteoarthritis(PMOA)by an ultrasound biomicroscopic technology.Twenty...This study aims to evaluate the therapeutic effects of laser acupuncture(LA)on articular cartilage degradation at the early stage of postmenopausal osteoarthritis(PMOA)by an ultrasound biomicroscopic technology.Twentyone healthy female Sprague-Dawley rats were randomly and evenly divided into three groups-sham,ovariectomized(OVX)and OVXþLA groups.Only the peri-ovarian fatty tissue of the sham rats was exteriorized.The animals in the OVX and OVXþLA groups underwent bilateral ovariectomy to create a menopause model.On day 8 after the ovariectomy,the 2-week LA therapy at acupoints Guanyuan(CV-4),Sanyinjiao(SP-6),and Zusanli(ST36)was performed in the OVXþLA group,while the sham and OVX groups had a sham treatment.All the animals were sacrificed on day 22.The cartilage tissues in the left knee joint were examined by ultrasound and histology.Compared with the sham group,the significantly degenerate alterations(p<0.01)in ultrasound roughness index(URI),reflection coefficient(RC1)and cartilage thickness were found in the OVX group,while the values of these parameters were significantly recovered(p<0.01)in the OVXþLA group.The LA treatment enhanced proteoglycans(PGs)content with a significant reduce of the modified Mankin score(p<0.01).The acoustic assessment strongly correlated with the histologic evaluation.The results showed that LA treatment could alleviate the postmenopause-induced cartilage degradation and promote tissue repair.LA has potential to be a simple and safe non-pharmacological countermeasure against PMOA for geriatric patient population.The ultrasound biomicroscopic technology could be a quantitative evaluation tool of the structural and morphological responses of articular cartilage to LA intervention.展开更多
文摘The project discusses the development of a deep learning model to detect osteoporosis from dental panoramic X-Ray images. It provides an in-depth understanding of human bone structure, osteoporosis, its symptoms, causes, prevalence, and risk factors. The project also explains bone density measurement using dual-energy X-ray absorptiometry (DEXA) and the application of artificial intelligence (AI) and machine learning (ML) in medical imaging. The study uses panoramic dental X-rays to evaluate AI technology in dental imaging and classification of mandible inferior cortical based on Klemetti and Kolmakow criteria. The model architecture consists of convolutional, pooling, fully connected, ReLU, and Softmax layers. Dropout and early stopping are added to the model. The training process uses the train-test approach with 100 epochs and a batch size of 32, and performance evaluation measures such as accuracy, sensitivity, specificity, and F1-score are used to assess the classifier’s performance. The findings and methodology provide a comprehensive understanding of the application of deep learning in the detection of osteoporosis from dental panoramic X-Ray images, and the study demonstrates a robust approach to implementing AI in medical imaging for osteoporosis detection.
文摘Computed tomography plays an important role in industrial non-destructive testing, medical applications, astronomy and many other fields to look inside the scanned object and to analysis its inner structures. A non-destructive testing software have been developed to efficiently detect inner flaws of space industrial components. As the core of our software, reconstruction algorithms including preprocess of raw data, re-arrange algorithm and filtered back-projection algorithms have been described in detail in this article. With real raw data from CASC of China, experimental results verified the applied reconstruction algorithm in our software. Furthermore, forward algorithms simulating generation of fan-beam raw data are also presented in this article.
基金supported by Indiana University Precision Health Initiative to KH and JZthe NSFC-Guangdong Joint Fund of China (Grant No. U1501256) to QFShenzhen Peacock Plan (Grant No. KQTD2016053112051497) to XZ and ND.
文摘Epithelial and stromal tissues are components of the tumor microenvironment and play a major role in tumor initiation and progression.Distinguishing stroma from epithelial tissues is critically important for spatial characterization of the tumor microenvironment.Here,we propose BrcaSeg,an image analysis pipeline based on a convolutional neural network(CNN)model to classify epithelial and stromal regions in whole-slide hematoxylin and eosin(H&E)stained histopathological images.The CNN model is trained using well-annotated breast cancer tissue microarrays and validated with images from The Cancer Genome Atlas(TCGA)Program.BrcaSeg achieves a classification accuracy of 91.02%,which outperforms other state-of-the-art methods.Using this model,we generate pixel-level epithelial/stromal tissue maps for 1000 TCGA breast cancer slide images that are paired with gene expression data.We subsequently estimate the epithelial and stromal ratios and perform correlation analysis to model the relationship between gene expression and tissue ratios.Gene Ontology(GO)enrichment analyses of genes that are highly correlated with tissue ratios suggest that the same tissue is associated with similar biological processes in different breast cancer subtypes,whereas each subtype also has its own idiosyncratic biological processes governing the development of these tissues.Taken all together,our approach can lead to new insights in exploring relationships between image-based phenotypes and their underlying genomic events and biological processes for all types of solid tumors.BrcaSeg can be accessed at https://github.com/Serian1992/ImgBio.
基金This work was supported by grants from the National Scientific Foundation of China(Nos.81871510,81471810)the Tianhe District Science and Technolo-gy Project of Guangzhou,Guangdong,China(No.201704KW026).
文摘Background: The zone of calcified cartilage (ZCC) plays an important role in the pathogenesis of osteoarthritis (OA) but has never been imaged in vivo with magnetic resonance (MR) imaging techniques. We investigated the feasibility of direct imaging of the ZCC in both cadaveric whole knee specimens and in vivo healthy knees using a 3-dimensional ultrashort echo time cones (3D UTE-Cones) sequence on a clinical 3T scanner. Methods: In all, 12 cadaveric knee joints and 10 in vivo healthy were collected. At a 3T MR scanner with an 8-channel knee coil, a fat-saturated 3D dual-echo UTE-Cones sequence was used to image the ZCC, following with a short rectangular pulse excitation and 3D spiral sampling with conical view ordering. The regions of interests (ROIs) were delineated by a blinded observer. Singlecomponent T2* and T2 values were calculated from fat-saturated 3D dual-echo UTE-Cones and a Carr-Purcell-Meiboom-Gill (T2 CPMG) data using a semi-automated MATLAB code. Results: The single-exponential fitting curve of ZCC was accurately obtained with R2 of 0.989. For keen joint samples, the ZCC has a short T2* ranging from 0.62 to 2.55 ms, with the mean ±standard deviation (SD) of 1.49 ±0.66 ms, and with 95% confidence intervals (CI) of 1.20-1.78 ms. For volunteers, the short T2* ranges from 0.93 to 3.52ms, with the mean±SD of 2.09±0.56 ms, and the 95% CI is 1.43 to 2.74ms in ZCC. Conclusions: The high-resolution 3D UTE-Cones sequence might be used to directly image ZCC in the human knee joint on a clinical 3T scanner with a scan time of more than 10 min. Using this non-invasive technique, the T2* relaxation time of the ZCC can be further detected.
基金supported by the National Natural Science Foundation of China(Grant Nos.51873137 and 61601317)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(Grant No.20KJB510001)。
文摘Conductive hydrogels are good candidates for flexible wearable sensors, which have received considerable attention for use in human-machine interfaces, human motion/health monitoring, and soft robots. However, these hydrogels often freeze at low temperatures and thus, exhibit low transparency, weak mechanical strength and stretchability, as well as poor adhesion strength.In this paper, conductive organohydrogels were prepared by thermal polymerization of acrylamide and N-(3-aminopropyl)methacrylamide in a glycerol-water binary solvent using Na Cl as a conductive salt. Compared to other organohydrogels, our organohydrogels featured higher fracture stress(170 kPa) and greater stretchability(900%). The organohydrogels showed excellent antifreezing properties and high transparency(97%, at 400–800 nm wavelength) and presented outstanding adhesion strength to a variety of substrates. The conductive organohydrogels that were stored at -20℃ for 24 h could still respond to both strain and pressure, showing a high sensitivity(gauge factor=2.73 under 100% strain), fast response time(0.4 s), and signal repeatability during multiple force cycles(~100 cycles). Furthermore, the conductivity of cleaved antifreezing gels could be restored by contacting the broken surfaces together. Finally, we used our organohydrogels to monitor human tremors and bradykinesia in real-time within wired and wireless models, thus presenting a potential application for Parkinson’s disease diagnosis.
基金supported by the National Basic Research Program of China (No. 2006CB705700)the National Natural Science Foundation of China (No.30970780)+3 种基金the Knowledge Innovation Project of the Chinese Academy of Sciences (No. KGCX2-YW-907)the Changjiang Scholars and Innovative Research Teamin University (No. IRT0645)the Chinese Academyof Sciences Hundred Talents Program, the Science and Technology Key Project of Beijing Municipal Education Commission (No. KZ200910005005)the Doctoral Fund of the Ministry of Education of China (No.20091103110005)
文摘We present a sparse Bayesian reconstruction method based on multiple types of a priori information for multispectral bioluminescence tomography （BLT）. In the Bayesian approach, five kinds of a priori information are incorporated, reducing the ill-posedness of BLT. Specifically, source sparsity characteristic is considered to promote reconstruction results. Considering the computational burden in the multispectral case, a series of strategies is adopted to improve computational efficiency, such as optimal permissible source region strategy and node model of the finite element method. The performance of the proposed algorithm is validated by a heterogeneous three-dimensional （3D） micron scale computed tomography atlas and a mouse-shaped phantom. Reconstructed results demonstrate the feasibility and effectiveness of the proposed algorithm.
基金supported by NSFC(11404207,11327405,11525416)Natural Science Foundation of Shanghai(14ZR1417500)Shanghai Colleges and Universities Young Teachers Training Funding Scheme(ZZsd115110,ZZsd115106)
文摘Evaluating bone regularly is important to prevent and control the disease of osteoporosis. Impact of osteoporosis on ultrasonic guided waves propagating in human long bones is studied in this paper. Multi-scale wavelet transform is proposed to process the received guided waves, and by analyzing energy changes in detail components of high order wavelet at different propagating distance to assess if osteoporosis happened. The guided waves signals are collected from the tibias of 13 volunteers. Based on the analysis of multi-scale wavelet transform, the high order detail components d6 and d5 changed dramatically with the propagation of ultrasonic guided waves along long bones, which means these 7 volunteers are diagnosed with osteoporosis. Compared with X-ray diagnosis, the effectiveness of this method can reach 92.3% in 13 volunteers. This suggests the multi-scale wavelet transform method is potential in ultrasonic assessment of bone quality.
文摘Background Precise estimation of current and future comorbidities of patients with cardiovascular disease is an important factor in prioritizing continuous physiological monitoring and new therapies.Machine learning(ML)models have shown satisfactory performance in short-term mortality prediction in patients with heart disease,whereas their utility in long-term predictions is limited.This study aimed to investigate the performance of tree-based ML models on long-term mortality prediction and effect of two recently introduced biomarkers on long-term mortality.Methods This study used publicly available data from the Collaboration Center of Health Information Appli-cation at the Ministry of Health and Welfare,Taiwan,China.The collected data were from patients admitted to the cardiac care unit for acute myocardial infarction(AMI)between November 2003 and September 2004.We collected and analyzed mortality data up to December 2018.Medical records were used to gather demo-graphic and clinical data,including age,gender,body mass index,percutaneous coronary intervention status,and comorbidities such as hypertension,dyslipidemia,ST-segment elevation myocardial infarction,and non-ST-segment elevation myocardial infarction.Using the data,collected from 139 patients with AMI,from medical and demographic records as well as two recently introduced biomarkers,brachial pre-ejection period(bPEP)and brachial ejection time(bET),we investigated the performance of advanced ensemble tree-based ML algorithms(random forest,AdaBoost,and XGBoost)to predict all-cause mortality within 14 years.A nested cross-validation was performed to evaluate and compare the performance of our developed models precisely with that of the conventional logistic regression(LR)as the baseline method.Results The developed ML models achieved significantly better performance compared to the baseline LR(C-Statistic,0.80 for random forest,0.79 for AdaBoost,and 0.78 for XGBoost,vs.0.77 for LR)(PRF<0.001,PAdaBoost<0.001,and PXGBoost<0.05).Adding bPEP and bET to our feature set significantly improved the performance of the algorithm,leading to an absolute increase in C-statistic of up to 0.03(C-statistic,0.83 for random forest,0.82 for AdaBoost,and 0.80 for XGBoost,vs.0.74 for LR)(PRF<0.001,PAdaBoost<0.001,PXGBoost<0.05).Conclusion The study indicates that incorporating new biomarkers into advanced ML models may significantly improve long-term mortality prediction in patients with cardiovascular diseases.This advancement may enable better treatment prioritization for high-risk individuals.
基金supported by the National Natural Science Foundation of China(11327405,11525416,11604054,11504057)
文摘In the application of cancellous bone ultrasound diagnosis based on backscattering method, it is of great importance to estimate fast and accurately whether the valid backscattering signal exists in the received signal. We propose a fast estimation method based on spectrum entropy method. With 984 records of adult calcaneus clinical data, we estimate the validity of the backscatter signal using this method. The results of the proposed method and the results of experience-base judgement were compared and analyzed. And two key parameters, the signal range length and the segment number of the spectrum entropy, were analyzed. The results show when the signal range length is 13 I^s and the segment number is 15 20, this method can get the best result （accuracy〉95%, sensitivity〉99%, specificity〉87%）, while taking little calculation time （1.5 ms）. Therefore, this spectrum entropy method can satisfy the accuracy and real-time requirements in the ultrasonic estimation for cancellous bone.
基金supported by the National Natural Science Foundation of China(Grant Nos.61601317 and 51873137)the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(Grant No.20KJB510001)+1 种基金the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)the National Key R&D Plan“Key Scientific Issues of Transformative Technology”(Grant No.2018YFA0701700)。
文摘In this paper,we propose a simple organohydrogel based capacitive humidity sensor for noncontact artificial sensation applications.The sensor is simple in design and consists of a transparent polyacrylamide organohydrogel thin film attached on a flexible inter-digit electrode layer.The process of water absorption and desorption is reversible,thus the dielectric of the organohydrogel film as well as the overall capacitance is dependent on environmental humidity.The water absorption capacity and structural reliability of the device have been largely improved by adding glycerol in the organohydrogel network.By optimizing both the glycerol concentration and organohydrogel film thickness,the sensor can respond to cyclic humidity changes in a period of 300 ms.In addition,this sensor achieves a high relative capacitance increase(by 20 folds)in a wide relative humidity range(12%-95%).The sensor also exhibits high stability under different bending curvatures(up to 6.81 mm),wide temperature changes(20℃-40℃)and external pressures(0-8 N).To demonstrate the applications in wearable electronics,we found that the sensor was successful in detecting respiration intensity and rate as well as the difference in moisture content in various objects,i.e.,human skin and leaf surface.This sensor is highly sensitive and can be useful in the detection of the widerange of humidity changes.
基金National Natural Science Foundation of China(No.81801653)
文摘To the Editor:Adipose tissue occurs in at least two different entities in mammals and humans:brown adipose tissue(BAT)and white adipose tissue(WAT).BAT is characterized by a unique uncoupling protein 1(UCP1)in the mitochondria that enables the uncoupling of the respiratory chain from adenosine triphosphate synthesis.Thus,energy is dissipated as heat to reduce fat accumulation.BAT is also considered a highly heterogeneous tissue with abundant oxygen,blood supply,and iron-rich mitochondria.[1,2]Activation of BAT via exposure to a cold environment is considered to be a means of reducing triglycerides to fight obesity.[3]The alterations in cells and tissues of activated BAT include increased iron content and UCP1 expression in mitochondrial,blood perfusion,and lipid utilization.[4]Therefore,accurate identification and quantitative analysis of inactive and activated BAT are of great significance for the treatment of metabolic diseases that target BAT,such as obesity.
基金Chair Professors of Changjiang Scholars Program and CAS Hundred Talents ProgramNational Program on Key Basic Research Projects (Grant No. 2006CB705700)+4 种基金National High-Tech R&D Program of China (Grant No.2006AA04Z216)National Key Technology R&D Program (Grant No. 2006BAH02A25) Joint Research Fund for Overseas Chinese Young Scholars (Grant No.30528027),National Natural Science Foundation of China (Grant Nos.30600151, 30500131 and 60532050) Natural Science Foundation of Beijing (Grant Nos. 4051002 and 4071003)
文摘With the rapid development of functional magnetic resonance imaging (fMRI) technology, the spatial resolution of fMRI data is continuously growing. This pro- vides us the possibility to detect the fine-scale patterns of brain activities. The es- tablished univariate and multivariate methods to analyze fMRI data mostly focus on detecting the activation blobs without considering the distributed fine-scale pat- terns within the blobs. To improve the sensitivity of the activation detection, in this paper, multivariate statistical method and univariate statistical method are com- bined to discover the fine-grained activity patterns. For one voxel in the brain, a local homogenous region is constructed. Then, time courses from the local ho- mogenous region are integrated with multivariate statistical method. Univariate statistical method is finally used to construct the interests of statistic for that voxel. The approach has explicitly taken into account the structures of both activity pat- terns and existing noise of local brain regions. Therefore, it could highlight the fine-scale activity patterns of the local regions. Experiments with simulated and real fMRI data demonstrate that the proposed method dramatically increases the sensitivity of detection of fine-scale brain activity patterns which contain the subtle information about experimental conditions.
基金supported by the program for Cheung Kong Scholars and Innovative Research Teamin University of China (No. IRT0645)the National Natural Science Foundation of China (No.60872137)+1 种基金the National Defence Foundation of China(No. 9140A01060408DZ0104)the National "973" Program of China (No. 2006CB705700)and the Programfor Chair Professors of Cheung Kong Scholars Program of China
文摘To avoid the ill-posedness in the inverse problem of bioluminescence tomography, a moment searching algorithm fusing the finite element method （FEM） with the moment concept in theoretical mechanics is developed. In the algorithm, the source＇s information is mapped to the surface photon flux density by FEM, and the source＇s position is modified with the feedback through the algorithm of barycenter searching, which makes full use of the position information of the photon flux density on surface. The position is modified in every iterative step and will finally converge to the real source＇s value theoretically.
基金supported by the National Natural Science Foundation of China(Nos.81871334,81801764,82072056,and 51937010)the Guangdong Basic and Applied Basic Research Foundation(Nos.2017A050506011,2018030310343,2020B1515020008,2021A1515012542,and 2021A1515011882)+1 种基金the Medical Scientific Research Foundation of Guangdong Province(No.A2018014)the Pearl River Talented Young Scholar Program(No.2017GC010282).
文摘Radiotherapy(RT)mediated tumor immunogenicity offers an opportunity for simultaneous RT and immunotherapy via immunogenic cell death(ICD),which releases damaged-associated molecular patterns and generates“eat me”signals for the innate immune system to modulate the immunogenicity.However,tumor hypoxia significantly reduces the therapeutic efficacy of RT and hampers its mediation of ICD induction.Herein,Au@Bi_(2)Te_(3)-polyethylene glycol(PEG)was rationally constructed as theranostic nanozymes for mild photothermal therapy,tumor hypoxia modulation,and RT adjuvant cancer immunotherapy.The tumor-specific production of oxygen could not only augment the effects of RT by enhanced reactive oxygen species(ROS)generation,but also reduce hypoxia-related cytokines and downregulate programmed cell death-ligand 1(PD-L1)to unleash immune-enhancing T cells.Moreover,Au@Bi_(2)Te_(3)-PEG could act as an immune-blocking inhibitor by efficient ICD induction with the combination of mild-photothermal therapy+RT to inhibit the tumor immune escape and improve antitumor immune response.Increased amounts of CD^(4+)and CD^(8+)Tcells and elevated levels of cytokines could be observed that eventually led to effective post-medication inhibition of primary and abscopal tumors.Spectral computed tomography/photoacoustic imaging allowed noninvasive and real-time tracking of nanoparticle(NP)accumulation and oxygenation status at tumor sites.Collectively,Au@Bi_(2)Te_(3)-PEG NPs could serve as effective theranostic nanoregulators with remarkable synergistic mildphotothermal/RT/immunotherapy effects that helped reshape the immune microenvironment and had remarkable molecular imaging properties.
基金This research was funded by National Key Research and Develop-ment Program of China(2018YFA0108700,2017YFA0105602)NSFC Projects of International Cooperation and Exchanges(81720108004)+3 种基金National Natural Science Foundation of China(81974019,81671749)The Research Team Project of Natural Science Foundation of Guangdong Province of China(2017A030312007)The key program of Guangzhou science research plan(201904020047)The Special Project of Dengfeng Program of Guangdong Provincial People’s Hospital(DFJH201812,KJ012019119,KJ012019423).
文摘Glutathione(GSH)is an important biological thiol in cells,which is involved in many physiological processes in the organism and regulates pathological processes of cells.Rapid and accurate monitoring of GSH in vitro and in vivo is quite needed in investigating important biochemical events.In this contribution,innovative cerium(Ce)doped polyaniline(Ce–Fe@PANI NPs)were prepared via Fe(III)induced oxidization polymerization method.Upon addition of GSH,the absorption of Ce–Fe@PANI NPs red shifted from the visible to the NIR region,con-firming the excellent absorption response to GSH.Moreover,Ce–Fe@PANI NPs exhibited excellent photoacoustic(PA)imaging enhancement in tube and shifted the PA intensity peak from 680 nm to 820 nm upon addition of GSH.In vitro and in vivo experiment verified that Ce–Fe@PANI NPs can monitor GSH in deep tissues via PA imaging technology.Collectively,this research provides Ce–Fe@PANI NPs would serve as a powerful nano-platform to realize PA imaging detection of GSH in vitro and in vivo.
基金the Special Funds of Guang Dong Universities for Talents(2050205)the Guangdong Science and Technology Program(grant no.2016A020216017)。
文摘This study aims to evaluate the therapeutic effects of laser acupuncture(LA)on articular cartilage degradation at the early stage of postmenopausal osteoarthritis(PMOA)by an ultrasound biomicroscopic technology.Twentyone healthy female Sprague-Dawley rats were randomly and evenly divided into three groups-sham,ovariectomized(OVX)and OVXþLA groups.Only the peri-ovarian fatty tissue of the sham rats was exteriorized.The animals in the OVX and OVXþLA groups underwent bilateral ovariectomy to create a menopause model.On day 8 after the ovariectomy,the 2-week LA therapy at acupoints Guanyuan(CV-4),Sanyinjiao(SP-6),and Zusanli(ST36)was performed in the OVXþLA group,while the sham and OVX groups had a sham treatment.All the animals were sacrificed on day 22.The cartilage tissues in the left knee joint were examined by ultrasound and histology.Compared with the sham group,the significantly degenerate alterations(p<0.01)in ultrasound roughness index(URI),reflection coefficient(RC1)and cartilage thickness were found in the OVX group,while the values of these parameters were significantly recovered(p<0.01)in the OVXþLA group.The LA treatment enhanced proteoglycans(PGs)content with a significant reduce of the modified Mankin score(p<0.01).The acoustic assessment strongly correlated with the histologic evaluation.The results showed that LA treatment could alleviate the postmenopause-induced cartilage degradation and promote tissue repair.LA has potential to be a simple and safe non-pharmacological countermeasure against PMOA for geriatric patient population.The ultrasound biomicroscopic technology could be a quantitative evaluation tool of the structural and morphological responses of articular cartilage to LA intervention.
