Search

Objective. To review the progress of research on photoplethysmography- (PPG-) based cuffless continuous blood pressure monitoring technologies and prospect the challenges that need to be addressed in the future. Methods. Using Web of Science and PubMed as search engines, the literature on cuffless continuous blood pressure studies using PPG signals in the recent five years were searched. Results. Based on the retrieved literature, this paper describes the available open datasets, commonly used signal preprocessing methods, and model evaluation criteria. Early researches employed multisite PPG signals to calculate pulse wave velocity or time and predicted blood pressure by a simple linear equation. Later, extensive researches were dedicated to mine the features of PPG signals related to blood pressure and regressed blood pressure by machine learning models. Most recently, many researches have emerged to experiment with complex deep learning models for blood pressure prediction with the raw PPG signal as input. Conclusion. This paper summarized the methods in the retrieved literature, provided insight into the artificial intelligence algorithms employed in the literature, and concluded with a discussion of the challenges and opportunities for the development of cuffless continuous blood pressure monitoring technologies.

Controlled fabrication of anisotropic materials has become a hotspot in materials science, particularly biomaterials, since the next generation of tissue engineering is based on the application of heterogeneous structures that can simulate the original biological complexity of the body. The current fabrication method of producing anisotropic materials involves expensive and highly specialized equipment, and not every conventional method can be applied to preparing anisotropic materials for corresponding tissue engineering. Anisotropic materials can be easily applied to a problem in tissue engineering: cartilage injury repairing. The articular cartilage consists of four spatially distinct regions: superficial, transitional, deep, and calcified. Each region has a specific extracellular matrix composition, mechanical properties, and cellular organization; this calls for the application of an anisotropic hydrogel. Controlled diffusion, under the assistance of buoyancy, has been considered a generalized method to prepare materials using a gradient. The diffusion of two solutions can be controlled through the difference in their densities. In addition to providing anisotropy, this method realizes the

Flexible wearable sweat sensors allow continuous, real-time, noninvasive detection of sweat analytes, provide insight into human physiology at the molecular level, and have received significant attention for their promising applications in personalized health monitoring. Electrochemical sensors are the best choice for wearable sweat sensors due to their high performance, low cost, miniaturization, and wide applicability. Recent developments in soft microfluidics, multiplexed biosensing, energy harvesting devices, and materials have advanced the compatibility of wearable electrochemical sweat-sensing platforms. In this review, we summarize the potential of sweat for medical detection and methods for sweat stimulation and collection. This paper provides an overview of the components of wearable sweat sensors and recent developments in materials and power supply technologies and highlights some typical sensing platforms for different types of analytes. Finally, the paper ends with a discussion of the challenges and a view of the prospective development of this exciting field.

Fluorescence probes with strong near-infrared (NIR) emission and water solubility are considered useful visualization tools for localization marking as well as investigating cell migration and tran...

Purpose Magnetoacoustic tomography with magnetic induction (MAT-MI) is a technique that utilizes the acoustic signals induced by magnetic stimulation to reconstruct the electrical impedance distribution in biological tissues. Most algorithms ignored the fact that acoustic properties in human tissues are heterogeneous, which lead to distortion and blurring of small reconstructed objects. In this study, a novel algorithm is proposed for exact reconstruction of the sound source distribution in acoustic heterogeneous tissues. Methods Based on the ring transducer array, we develop an algorithm which combines algebraic reconstruction technique (ART) and time reversal method. Different to existing reconstruction methods, the ultrasonic transmission tomography (UTT) and the MAT-MI can be completed in same system, which decreases the system complexity. The sound velocity distribution is reconstructed with the ART so that the propagation time of the magnetoacoustic signals in the heterogeneous tissue is corrected. And then, the sound source image is reconstructed based on the time reversal method from new sound pressure data. Both numerical simulations and phantom experiments are established to validate the proposed method. Results Compared with the results without consideration of the variation on acoustic speed, sound sources reconstructed by our method are more consistent with the model in terms of size and shape. Conclusions The novel algorithm can be used to reconstruct the high-accuracy image of MAT-MI sound source in the sound velocity inhomogeneous media. In addition, this method is applicable to scenarios that the prior knowledge of the imaging targets is unknown. The signal acquisition time of MAT-MI in acoustically heterogeneity media is greatly reduced due to the introduction of ring transducer array into the imaging system. Therefore, our method will promote the application of MAT-MI in noninvasive early diagnosis of tumor for preclinical study.

Our understanding of how aging affects the cellular and molecular components of the vasculature and contributes to cardiovascular diseases is still limited. Here we report a single-cell transcriptomic survey of aortas and coronary arteries in young and old cynomolgus monkeys. Our data define the molecular signatures of specialized arteries and identify eight markers discriminating aortic and coronary vasculatures. Gene network analyses characterize transcriptional landmarks that regulate vascular senility and position FOXO3A, a longevity-associated transcription factor, as a master regulator gene that is downregulated in six subtypes of monkey vascular cells during aging. Targeted inactivation of FOXO3A in human vascular endothelial cells recapitulates the major phenotypic defects observed in aged monkey arteries, verifying FOXO3A loss as a key driver for arterial endothelial aging. Our study provides a critical resource for understanding the principles underlying primate arterial aging and contributes important clues to future treatment of age-associated vascular disorders., Arterial degeneration, closely associated with cardiovascular diseases, is driven by aging-related vascular cell-specific transcriptomics changes. This study provides a single-cell transcriptomic atlas for senile aortic and coronary arteries and underscores FOXO3A-based the transcriptional network in vasoprotection during aging.

Background Prostate cancer (PCa) is a common malignant tumor of the urinary system in males. LncRNA taurine-upregulated gene 1 (TUG1) has been verified to play a crucial role in progression and prognosis of PCa. However, the functional mechanism of TUG1 remains unclear with radiosensitivity of PCa. Methods Quantitative real-time PCR (qRT-PCR) was conducted to measure the transcription levels of genes. 3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide (MTT) assay and flow cytometry analysis were employed to assess cell proliferation and apoptosis, respectively. Moreover, colony formation assay was used to measure colony survival. Western blot was performed to detect the relative proteins expression. The interaction among variables was predicted by online tool starbase, and then confirmed using the dual luciferase reporter assay. A xenograft mouse model was constructed to investigate the effect of TUG1 on tumor growth in vivo. Results The levels of lncRNA TUG1 and SMC1A were remarkably increased, while miR-139-5p was downregulated in PCa. Patients with high expression of TUG1 showed a lower survival rate and poor prognosis. Knockdown of TUG1 inhibited PCa cell proliferation and colony survival fraction, and promoted apoptosis. Downregulation of miR-139-5p reversed the effects of TUG1 deletion on proliferation, apoptosis and colony survival fraction in PCa cells treated with 4 Gy of X-ray radiation. Moreover, TUG1 sponged miR-139-5p to regulate SMC1A expression. SMC1A deletion blocked the effects of TUG1 on the progression of PCa cells treated with 4 Gy of X-ray radiation. The tumor volume and weight were illustriously reduced with radiation and TUG1 silencing in xenograft model. Conclusion Knockdown of lncRNA TUG1 enhanced radiosensitivity in PCa via the TUG1/miR-139-5p/SMC1A axis. It may become a promising target for PCa treatment.

U-Net has achieved great success in the task of medical image segmentation. It encodes and extracts information from several convolution blocks, and then decodes the feature maps to get the segmentation results. Our experiments show that in a multi-scale medical segmentation task, excessive downsampling will cause the model to ignore the small segmentation objects and thus fail to complete the segmentation task. In this work, we propose a more complete method Double-branch U-Net (2BUNet) to solve the multi-scale organ segmentation challenge. Our model is divided into four parts: main branch, tributary branch, information exchange module and classification module. The main advantages of the new model consist of: (1) Extracting information to improve model decoding capabilities using the complete encoding structure. (2) The information exchange module is added to the main branch and tributaries to provide regularization for the model, so as to avoid the large gap between the two paths. (3) Main branch structure for extracting major features of large organ. (4) The tributary structure is used to enlarge the image to extract the microscopic characteristics of small organ. (5) A classification assistant module is proposed to increase the class constraint for the output tensor. The comparative experiments show that our method achieves state-of-the-art performances in real scenes.

Craniopharyngioma is a kind of benign brain tumor in histography. However, it might be clinically aggressive and have severe manifestations, such as increased intracranial pressure, hypothalamic-pituitary dysfunction, and visual impairment. It is considered challenging for radiologists to predict the invasiveness of craniopharyngioma through MRI images. Therefore, developing a non-invasive method that can predict the invasiveness and boundary of CP as a reference before surgery is of clinical value for making more appropriate and individualized treatment decisions and reducing the occurrence of inappropriate surgical plan choices.The MT-Brain system has consisted of two pathways, a sub-path based on 2D CNN for capturing the features from each slice of MRI images, and a 3D sub-network for capturing additional context information between slices. By introducing the two-path architecture, our system can make full use of the fusion of the above 2D and 3D features for classification. Furthermore, position encoding and mask-guided attention also have been introduced to improve the segmentation and diagnosis performance. To verify the performance of the MT-Brain system, we have enrolled 1032 patients with craniopharyngioma (302 invasion and 730 non-invasion patients), segmented the tumors on postcontrast coronal T1WI and randomized them into a training dataset and a testing dataset at a ratio of 8:2.The MT-Brain system achieved a remarkable performance in diagnosing the invasiveness of craniopharyngioma with the AUC of 83.84%, the accuracy of 77.94%, the sensitivity of 70.97%, and the specificity of 80.99%. In the lesion segmentation task, the predicted boundaries of lesions were similar to those labeled by radiologists with the dice of 66.36%. In addition, some explorations also have been made on the interpretability of deep learning models, illustrating the reliability of the model.To the best of our knowledge, this study is the first to develop an integrated deep learning model to predict the invasiveness of craniopharyngioma preoperatively and locate the lesion boundary synchronously on MRI. The excellent performances indicate that the MT-Brain system has great potential in real-world clinical applications.

High-quality multimodal imaging requires exogenous contrast agents with high sensitivity, spatial-temporal resolution, and high penetration depth for the accurate diagnosis and surveillance of cancer. Herein, we design a highly efficient fluorescent and magnetic multimodal probe by doping fluorescent molecule 2-(4-bromophenyl)-3-(4-(4-(diphenylamino)styryl)phenyl)fumaronitrile (TB) with near-infrared (NIR) fluorescent emission (714 nm) and superparamagnetic iron oxide (SPIO) into a polystyrene-polyethylene glycol (PS-PEG) matrix to form TB/SPIO@PS-PEG nanoparticles (TSP NPs). The as-prepared TSP NPs exhibit red aggregation-induced emission (AIE) with a maximum wavelength at 655 nm and high fluorescence quantum yield (QY) of 14.6%, facilitating the improvement of sensitivity and signal-to-background ratio for fluorescence molecular imaging (FMI). Phase behavior investigation of the TB/SPIO@PS-PEG probe system by Flory-Huggins lattice theory elucidates the highly efficient fluorescence of the multimodal probe, originating from the poor miscibility between TB and SPIO. Meanwhile, the TSP NPs possess good superparamagnetism and relaxivity and can thus be used as appropriate magnetic contrast agents for magnetic resonance imaging (MRI) and magnetic particle imaging (MPI). In addition, the good biocompatibility and photostability of the TSP NPs make them suitable for long-term monitoring. In vivo fluorescence imaging results indicate that the TSP NPs can facilitate monitoring of subcutaneous tumor growth for more than 24 days in a real-time manner. Multimodal imaging consisting of FMI, MRI, and MPI reveals that TSP NPs can monitor a liver tumor in situ with almost unlimited depth in tissues and high temporal-spatial resolution. As a multimodal probe, the TSP NPs manifest obvious synergistic advantages of long-term monitoring and high penetration depth and hold great prospects in tumor imaging.

Facial age estimation is an important yet very challenging problem in computer vision. To improve the performance of facial age estimation, we first formulate a simple standard baseline and build a much strong one by collecting the tricks in pre-training, data augmentation, model architecture, and so on. Compared with the standard baseline, the proposed one significantly decreases the estimation errors. Moreover, long-tailed recognition has been an important topic in facial age datasets, where the samples often lack on the elderly and children. To train a balanced age estimator, we propose a two-stage training method named Long-tailed Age Estimation (LAE), which decouples the learning procedure into representation learning and classification. The effectiveness of our approach has been demonstrated on the dataset provided by organizers of Guess The Age Contest 2021.

Objective. Fluorescence molecular tomography (FMT) is a promising non-invasive optical molecular imaging technology with strong specificity and sensitivity that has great potential for preclinical and clinical studies in tumor diagnosis, drug development and therapeutic evaluation. However, the strong scattering of photons and insufficient surface measurements make it very challenging to improve the quality of FMT image reconstruction and its practical application for early tumor detection. Therefore, continuous efforts have been made to explore more effective approaches or solutions in the pursuit of high-quality FMT reconstructions. Approach. This review takes a comprehensive overview of advances in imaging methodology for FMT, mainly focusing on two critical issues in FMT reconstructions: improving the accuracy of solving the forward physical model and mitigating the ill-posed nature of the inverse problem from a methodological point of view. More importantly, numerous impressive and practical strategies and methods for improving the quality of FMT reconstruction are summarized. Notably, deep learning methods are discussed in detail to illustrate their advantages in promoting the imaging performance of FMT thanks to large datasets, the emergence of optimized algorithms and the application of innovative networks. Main results. The results demonstrate that the imaging quality of FMT can be effectively promoted by improving the accuracy of optical parameter modeling, combined with prior knowledge, and reducing dimensionality. In addition, the traditional regularization-based methods and deep neural network-based methods, especially end-to-end deep networks, can enormously alleviate the ill-posedness of the inverse problem and improve the quality of FMT image reconstruction. Significance. This review aims to illustrate a variety of effective and practical methods for the reconstruction of FMT images that may benefit future research. Furthermore, it may provide some valuable research ideas and directions for FMT in the future, and could promote, to a certain extent, the development of FMT and other methods of optical tomography.

The whole physiological state of cardiac electrical activity is reflected by electrocardiogram (ECG) signals collected from the body surface. ECG signals can be used as important basis for the diagnosis of heart disease. However, when analyzing ECG signals, people often process the signals in a record into units of heartbeats. Therefore, accurate heartbeat detection is an important prerequisite for the correct diagnosis of heart disease by the machine. The traditional heartbeat detection only obtains the QRS complex based on the Pan-Tompkins algorithm, and then performs time interception based on previous experience (forward and backward). The accuracy of heartbeats detection will be affected by individual differences. In this paper, an optimized Pan-Tompkins algorithm based on adding P-wave and T-wave location is proposed for the detection of ECG heartbeats. This algorithm was evaluated using 109966 samples from the MIT- BIH Arrhythmia Database including a variety of arrhythmia data (such as atrioventricular block, ventricular tachycardia, early ventricular contraction, atrial flutter, ventricular premature beats, ventricular fibrillation). 109519 samples were correctly detected from 109966 heartbeats. This method achieved 99.68% positive prediction rate(P +) with 99.90% sensitivity)SE). Our approach will lay a foundation for the processing of ECG signals and therefore improve the accuracy for the diagnosis of heart diseases.

Stem cell therapies are increasingly recognized as the future direction of regenerative medicine, but the biological fate of the administrated stem cells remains a major concern for clinical translation, which calls for an approach to efficiently monitoring the stem cell behaviors in vivo. Magnetic particle imaging (MPI) is an emerging technology for cell tracking; however, its utility has been largely restricted due to the lack of optimal magnetic nanoparticle tracers. Herein, by controlled engineering of the size and shape of magnetic nanoparticles tailored to MPI physics theory, a specialized MPI tracer, based on cubic iron oxide nanoparticles with an edge length of 22 nm (CIONs-22), is developed. Due to the inherent lower proportion of disordered surface spins, CIONs-22 exhibit significantly larger saturation magnetization than that of spherical ones, while they possess similar saturation magnetization but smaller coercivity compared to larger-sized CIONs. These magnetic properties of CIONs-22 warrant high sensitivity and resolution of MPI. With their efficient cellular uptake, CIONs-22 exhibit superior MPI performance for stem cell labeling and tracking compared to the commercialized tracer Vivotrax. By virtue of these advantages, CIONs-22 enable real-time and prolonged monitoring of the spatiotemporal trajectory of stem cells transplanted to hindlimb ischemia mice, which demonstrates the great potential of CIONs-22 as MPI tracers to advance stem cell therapies.

Pseudomonas aeruginosa-mannose-sensitive hemagglutinin (PAM) is an inactivate P. aeruginosa with mannose-sensitive hemagglutinin. Recently, the anticancer properties of PAM against many cancers have been reported across a range of studies. However, the exact mechanism through which PAM prevents skin cancer remains unclear. The aim of this study is to show to what extent PAM could inhibit the dimethylbenzanthracene (DMBA)/12-O-tetradecanoylphorbol-13-acetate (TPA)-induced skin cancer. JB6 cells were treated by TPA so as to establish an in vitro model. The effects of PAM on proliferation of the cells were analyzed using cell counting kit-8 assays. Effects on epithelial–mesenchymal transition (EMT) were assayed by real-time PCR and Western blotting. A DMBA/TPA-induced skin cancer mouse model was also established. The results showed that TPA promoted EMT changes through the activation of the hedgehog (Hh) pathway, which was reversed by PAM. Moreover, PAM inhibited the cancer growth and Hh pathway in vivo. These data indicate that PAM may serve as a potential anticancer agent for the treatment of skin cancer. Impact statement Pseudomonas aeruginosa-mannose-sensitive hemagglutinin (PAM) restrained the chemical-induced skin cancer cells in vitro and in vivo partly through suppressing the Hh signaling pathway, indicating that PAM may be a promising anticancer agent for treating skin cancer.

Early detection and therapy of esophageal cancer is very important for improving the prognosis and survival rate of the patient. A theranostic agent that combines multimodal imaging with cancer therapy may be used for augmenting the visualization and treatment of the cancer. Herein, we report the synthesis of a hollow tantalum oxide (TaOx) nanoparticle that was successfully engineered by encapsulation of polypyrrole (PPy) and doxorubicin (DOX) in the core and conjugation with a near infrared fluorescence dye (NIRDye800) on the shell of the hollow TaOx nanoparticles. The as-prepared core/shell nanoparticles showed multimodal imaging features including computed tomography (CT) (for the preliminary location of the tumor), photoacoustic (for the anatomical localization of the tumor), and fluorescence imaging (for real-time monitoring of the tumor margin) and pH- and thermal-sensitive drug release. Because the early esophageal carcinoma is a type of superficial cancer, a subcutaneous model in the thigh was used for the in vivo study. The core/shell nanoparticles shows high imaging contrast between the tumor and the adjacent tissues and controllable photothermal therapy (PTT) and chemotherapy. Our results indicated that the obtained core/shell nanoparticles had significant potential in the triple-modality imaging guided precisely chemo-thermal synergetic therapy of esophageal cancer. In addition, after aerosol administration, our nanoparticles also exhibited comparable therapeutic efficacy with the intravenous administration, which is more suitable for clinical therapy of esophageal carcinoma.

The last couple of decades have seen brilliant progress in stem cell therapies, including native, genetically modified, and engineered stem cells, for osteonecrosis of the femoral head (ONFH). In vitro studies evaluate the effect of endogenous or exogenous factor or gene regulation on osteogenic phenotype maintenance and/or differentiation towards osteogenic lineage. The preclinical and clinical outcomes accelerate the clinical translation. Bone marrow mesenchymal stem cells and adipose-derived stem cells have demonstrated better effects in the treatment of femoral head necrosis. Various materials have been used widely in the ONFH treatment in both preclinical and clinical trials. In a word, in vivo and multiscale efforts are expected to overcome obstacles in the approaches for treating ONFH and provide clinical relevance and commercial strategies in the future. Therefore, we will discuss the above aspects in this paper and present our opinions.

SM5-1 is a humanized mouse monoclonal antibody, targeting an over-expressed membrane protein of approximately 230 kDa in hepatocellular carcinoma (HCC). SM5-1 can be used for target therapy in hepatocellular carinoma due to its ability of inhibiting cell growth and inducing apoptosis. However, the tumor inhibition efficacy of SM5-1 in HCC cancer treatment remains low. In this study, we synthesized SM5-1-conjugated gold nanoparticles (Au-SM5-1 NPs) and investigated their anticancer efficacy in HCC both in vitro and in vivo. The tumor inhibition rates of Au-SM5-1 NPs for subcutaneous tumor mice were 40.10% ± 4.34%, 31.37% ± 5.12%, and 30.63% ± 4.87% on day 12, 18, and 24 post-treatment as determined by bioluminescent intensity. In addition, we investigated the antitumor efficacy of Au-SM5-1 NPs in orthotopic HCC tumor models. The results showed that the inhibition rates of Au-SM5-1 NPs can reach up to 39.64% ± 4.87% on day 31 post-treatment determined by the bioluminescent intensity of the abdomen in tumor-bearing mice. Furthermore, three-dimensional reconstruction results of the orthotopic tumor revealed that Au-SM5-1 NPs significantly inhibited tumor growth compared with SM5-1 alone. Our results suggested that the developed Au-SM5-1 NPs has great potential as an antibody-based nano-drug for HCC therapy.

A high-strength AZ80 Mg alloy was prepared via multi-direction forge, thermal extrusion and peak-aged heat treatment. The corrosion behaviour of AZ80 Mg/beryllium–bronze–alloy galvanic couples in a typical marine environment was investigated. Beryllium–bronze alloy (QBe1.7 Cu) acted as cathode, accelerating the corrosion of AZ80 Mg alloy. With the increase in contact areas, the corrosion rates and galvanic effect also increased, and the galvanic potentials moved positively. However, with a prolonged exposure time, the galvanic potentials and current densities decreased. The corrosion products mainly consisted of MgCl2, Mg5(CO3)4(OH)2·5H2O and MgSO4·7H2O. According to the standard of galvanic corrosion sensitivity from the Air Force Materials Laboratory (AFML), AZ80 Mg alloy was not allowed to make contact with QBe1.7 Cu alloy without effective protection.

Ferroptosis, an iron-based cell-death pathway, has recently attracted great attention owing to its effectiveness in killing cancer cells. Previous investigations focused on the development of iron-based nanomaterials to induce ferroptosis in cancer cells by the up-regulation of reactive oxygen species (ROS) generated by the well-known Fenton reaction. Herein, we report a ferroptosis-inducing agent based on arginine-rich manganese silicate nanobubbles (AMSNs) that possess highly efficient glutathione (GSH) depletion ability and thereby induce ferroptosis by the inactivation of glutathione-dependent peroxidases 4 (GPX4). The AMSNs were synthesized via a one-pot reaction with arginine (Arg) as the surface ligand for tumor homing. Subsequently, a significant tumor suppression effect can be achieved by GSH depletion-induced ferroptosis. Moreover, the degradation of AMSNs during the GSH depletion contributed to T1-weighted magnetic resonance imaging (MRI) enhancement as well as on-demand chemotherapeutic drug r...

Tumor cell complete extinction is a crucial measure to evaluate antitumor efficacy. The difficulties in defining tumor margins and finding satellite metastases are the reason for tumor recurrence. A synergistic method based on multimodality molecular imaging needs to be developed so as to achieve the complete extinction of the tumor cells. In this study, graphene oxide conjugated with gold nanostars and chelated with Gd through 1,4,7,10-tetraazacyclododecane-N,N′,N,N′-tetraacetic acid (DOTA) (GO-AuNS-DOTA-Gd) were prepared to target HCC-LM3-fLuc cells and used for therapy. For subcutaneous tumor, multimodality molecular imaging including photoacoustic imaging (PAI) and magnetic resonance imaging (MRI) and the related processing techniques were used to monitor the pharmacokinetics process of GO-AuNS-DOTA-Gd in order to determine the optimal time for treatment. For orthotopic tumor, MRI was used to delineate the tumor location and margin in vivo before treatment. Then handheld photoacoustic imaging system was used to determine the tumor location during the surgery and guided the photothermal therapy. The experiment result based on orthotopic tumor demonstrated that this synergistic method could effectively reduce tumor residual and satellite metastases by 85.71% compared with the routine photothermal method without handheld PAI guidance. These results indicate that this multimodality molecular imaging-guided photothermal therapy method is promising with a good prospect in clinical application.

Fluorescence molecular tomography (FMT), as a promising imaging modality in preclinical research, can obtain the three-dimensional location information of the specific tumor for small animal imaging. A lot of methods based on orthogonal matching pursuit (OMP) have been used for FMT reconstruction. OMP and most of its variants use the mean square error criterion to estimate the sparse vector which depends on the Gaussian assumption of the error distribution. However, this method has poor performance on reconstruction with non-Gaussian noise. In this study, we propose correntropy matching pursuit (CMP) method to alleviate this problem of OMP. Unlike some other matching pursuit methods, CMP method is independent of the error distribution. This method can assign small weights on severely corrupted entries of data and large weights on clean ones adaptively, thus reducing the effect of large noise. The Gaussian assumption of the noise distribution is often used in the matching pursuit (MP) methods so that they can't work well with the non- Gaussian noise. Our method is independent of the noise distribution and has better performance on FMT reconstruction. Compared with StOMP and SASP, CMP method shows better performance on FMT reconstruction with non-Gaussian noise and similar performance on FMT.

SM5-1 is a humanized mouse antibody which has a high binding specificity for a membrane protein of about 230 kDa overexpressed in hepatocellular carcinoma (HCC), melanoma and breast cancer. In this study, SM5-1-conjugated poly d, l (lactide-coglycolide) (PLA) PLA containing Cy7 (PLA-Cy7-SM5-1) was prepared to study the targeting specificity of the bioconjugate to HCC-LM3-fLuc cell. Then, SM5-1-conjugated PLA containing 5-fluorouracil (5-FU) (PLA-5FU-SM5-1) and PLA containing 5-FU (PLA-5FU) were prepared for treatment of subcutaneous HCC-LM3-fLuc tumor mice. The results showed that PLA-5FU-SM5-1, PLA-5FU and 5-FU induced a 45.07%, 23.56% and 19.05% tumor growth inhibition rate, respectively, on day 31 post-treatment as determined by bioluminescent intensity. In addition, in order to evaluate the antitumor efficacy of PLA-5FU-SM5-1, HCC-LM3-fLuc cells were injected into the liver to establish the experimental orthotopic liver tumor models. The experiments showed that PLA-5FU-SM5-1, PLA-5FU and 5-FU induced a 53.24%, 31.00%, and 18.11% tumor growth inhibition rate, respectively, on day 31 post-treatment determined by the bioluminescent intensity of the abdomen in tumor-bearing mice. Furthermore, we have calculated the three-dimensional location of the liver cancer in mice using a multilevel adaptive finite element algorithm based on bioluminescent intensity decay calibration. The reconstruction results demonstrated that PLA-5FU-SM5-1 inhibited the tumor rapid progression, which were consistent with the results of subcutaneous tumor mice experiments and in vitro cell experiment results.

In this study, a fast and robust reconstruction method based on the separable approximation and the adaptive regularization is presented for fluorescence molecular tomography. The subproblems can be established and solved efficiently through separable approximation, and the convergence process can be also accelerated by adaptive regularization. As is well known, the regularization parameter has an important impact on the results, and finding the optimal or near-optimal regularization parameter automatically is an challenging task. To solve this problem, the regularization parameter in the proposed method is updated heuristically instead of being determined manually or empirically. This adaptive regularization strategy of the proposed method can perform accurate reconstruction almost without worrying about the choice of the regularization parameter. By contrast, improper choice of the regularization parameter may cause larger location errors for the three contrasting methods. The proposed method is proved robust and insensitive to parameters, which can improve the reconstruction accuracy. Moreover, the proposed method was about 1-2 orders of magnitude faster than the contrasting methods commonly used in fluorescence tomography reconstruction. Furthermore, reliable performance on different initial unknown values and different noise levels was also investigated. Finally, the potential of the proposed method in a practical application was further validated by the physical experiment with a mouse model. (C) 2012 Elsevier B.V. All rights reserved.