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In this work, a sensor array consisting of four sensors is designed. Detection of any mixture concentration in the range of 0–450 ppm hydrogen, 0–450 ppm ethylene, and 0–450 ppm carbon monoxide were achieved. Filter processing reduces the influence of sensor noise and ensures high recognition accuracy. Then, a total of 48 features such as response value, time feature, and first- and second-order features of sensor resistance were extracted from four sensors in each sample. In this article, the first- and second-order features are creatively proposed, which have a significant impact on the accuracy of the later model. It is found that gradient boosting decision tree (GBDT) has the highest classification accuracy, reaching 100%. Compared to models trained using only the first 180 s of data, gas prediction can be achieved at the initial sensor response stage, and the GBDT model has a preparation rate of up to 99.64%. To study the influence of different features on the sensor, the feature contribution of selected features in GBDT method is described. For the classification tasks, response time, recovery time, and second-order features contribute the most. Finally, the regression designed by GBDT method has a root-mean-square error of 23.52 ppm and an accuracy of 94.77%. This method did not require a lot of calculation and was suitable for running in small-embedded devices.

Ageing is a critical factor in spinal-cord-associated disorders1, yet the ageing-specific mechanisms underlying this relationship remain poorly understood. Here, to address this knowledge gap, we combined single-nucleus RNA-sequencing analysis with behavioural and neurophysiological analysis in non-human primates (NHPs). We identified motor neuron senescence and neuroinflammation with microglial hyperactivation as intertwined hallmarks of spinal cord ageing. As an underlying mechanism, we identified a neurotoxic microglial state demarcated by elevated expression of CHIT1 (a secreted mammalian chitinase) specific to the aged spinal cords in NHP and human biopsies. In the aged spinal cord, CHIT1-positive microglia preferentially localize around motor neurons, and they have the ability to trigger senescence, partly by activating SMAD signalling. We further validated the driving role of secreted CHIT1 on MN senescence using multimodal experiments both in vivo, using the NHP spinal cord as a model, and in vitro, using a sophisticated system modelling the human motor-neuron–microenvironment interplay. Moreover, we demonstrated that ascorbic acid, a geroprotective compound, counteracted the pro-senescent effect of CHIT1 and mitigated motor neuron senescence in aged monkeys. Our findings provide the single-cell resolution cellular and molecular landscape of the aged primate spinal cord and identify a new biomarker and intervention target for spinal cord degeneration.

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.

It remains an unresolved challenge to achieve spatial and temporal monitoring of drug release from nanomedicines (NMs) in vivo, which is of crucial importance in disease treatment. To tackle this issue, we constructed core–satellite ICG/DOX@Gel-CuS NMs, which consist of gelatin (Gel) nanoparticles (NPs) with payloads of near-infrared fluorochrome indocyanine green (ICG) and chemo-drug doxorubicin (DOX) and surrounding CuS NPs. The fluorescence of ICG was initially shielded by satellite CuS NPs within the intact ICG/DOX@Gel-CuS NMs and increased in proportion to the amount of DOX released from NMs in response to enzyme-activated NMs degradation. For more comprehensive understanding of the drug-release profile, a theoretical model derived from computer simulation was employed to reconstruct the enzyme-activatable drug release of the ICG/DOX@Gel-CuS NMs, which demonstrated the underlying kinetics functional relationship between the released DOX amount and recovered ICG fluorescence intensity. The kinetics of drug release in vivowas assessed by administrating ICG/DOX@Gel-CuS NMs both locally and systemically into MDA-MB-231 tumor-bearing mice. Upon accumulation of ICG/DOX@Gel-CuS NMs in the tumor, overexpressed enzymes triggered the degradation of the gelatin scaffold as well as the release of DOX and ICG, which can be visually depicted with the ICG fluorescence signal increasing only in the tumor area by fluorescence imaging. Additionally, the photoacoustic signal from CuS NPs was independent from the physical status of ICG/DOX@Gel-CuS NMs and hence was utilized for real-time NMs tracking. Thus, by taking advantage of the core–satellite architecture and NMs degradability in tumor site, the DOX release profile of ICG/DOX@Gel-CuS NMs was monitored by fluorescence and photoacoustic dual-modal imaging in a real-time noninvasive manner.

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 viaa 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 release for synergistic cancer therapy. We anticipate that the GSH-depletion-induced ferroptosis strategy by using manganese-based nanomaterials would provide insights in designing nanomedicines for tumor-targeted theranostics.

The primate frontal lobe (FL) is highly sensitive to aging-related neurocognitive decline. However, the aging-associated molecular mechanisms remain unclear. Here, using physiologically aged non-human primates (NHPs), we depicted a comprehensive landscape of FL aging with multidimensional profiling encompassing bulk and single-nucleus transcriptomes, quantitative proteome, and DNA methylome. Conjoint analysis across these molecular and neuropathological layers underscores nuclear lamina and heterochromatin erosion, resurrection of endogenous retroviruses (ERVs), activated pro-inflammatory cyclic GMP-AMP synthase (cGAS) signaling, and cellular senescence in post-mitotic neurons of aged NHP and human FL. Using human embryonic stem-cell-derived neurons recapitulating cellular aging in vitro, we verified the loss of B-type lamins inducing resurrection of ERVs as an initiating event of the aging-bound cascade in post-mitotic neurons. Of significance, these aging-related cellular and molecular changes can be alleviated by abacavir, a nucleoside reverse transcriptase inhibitor, either through direct treatment of senescent human neurons in vitroor long-term oral administration to aged mice.

Optical molecular imaging has been frequently used in many preclinical researches including cancer detection, tumor mechanism study, drug efficacy evaluation and targeted drug delivery. Among optical molecular imaging modalities, bioluminescence imaging and fluorescence imaging have acquired certain degree of development and attracted more and more attention in recent years for their high sensitivity and low cost. With the development of optical technology and probe technology, two-photon microscopy imaging and Raman imaging have been widely used in drug evaluation and tumor studies through cell and tissue imaging. This paper focuses on the occurrence and development of optical molecular imaging especially for its imaging method imaging system and its application in target drug delivery. Finally, the existing challenges and brand new applications of optical imaging techniques in drug delivery research are discussed.

An iterative adaptive weighted filtered backprojection (FBP) approach was applied to our photoacoustic imaging (PAI) of the optical absorption in biological tissues from limited-view data. By using an image-based adaptive weighted PAI reconstruction, we can modify the defect of the artifacts degrading the quality of the image. Results of numerical simulations demonstrated that the proposed algorithm was superior to FBP in terms of both accuracy and robustness to noise. Reconstructed images of biological tissues agreed well with the structures of the samples. The resolution of the PAI system with the proposed method was experimentally demonstrated to be better than 0.14 mm. By using the proposed method, the imaging quality of the PAI system can be improved.

Determining an appropriate regularization parameter is often challenging work because it has a narrow range and varies with problems, which is likely to lead to large reconstruction errors. In this contribution, an adaptive regularized method based on homotopy is presented for sparse fluorescence tomography reconstruction. Due to the adaptive regularization strategy, the proposed method is always able to reconstruct sources accurately independent of the estimation of the regularization parameter. Moreover, the proposed method is about two orders of magnitude faster than the two contrasting methods. Numerical and in vivo mouse experiments have been employed to validate the robustness and efficiency of the proposed method.

Bioluminescence tomography (BLT) can three-dimensionally and quantitatively resolve the molecular processes in small animals in vivo. In this paper, we propose a BLT reconstruction algorithm based on duality and variable splitting. By using duality and variable splitting to obtain a new equivalent constrained optimization problem and updating the primal variable as the Lagrangian multiplier in the dual augmented Lagrangian problem, the proposed method can obtain fast and stable source reconstruction even without the permissible source region and multispectral measurements. Numerical simulations on a mouse atlas and in vivo mouse experiments were conducted to validate the effectiveness and potential of the method.

Photoacoustic imaging is a noninvasive biomedical imaging modality for visualizing the internal structure and function of soft tissues. In this paper we present a combined iterative reconstruction algorithm to recover the distribution of optical absorption coefficients. Such a combination of the simultaneous algebraic reconstruction technique (SART) algorithm and the filtered back projection (FBP) algorithm bears the promise to improve the image quality of fast but not accurate. The different between the present proposal and traditional ART algorithm is that we computer a full error image with FBP, which is used to projection differences between original and reconstruction image, and to update the solution in each iteration step. The combined iterative reconstruction algorithm works well in rectification of the measurement and converges quickly onto an accurate estimate of the distribution of absolute absorption coefficients. Numerical simulations demonstrate that the proposed algorithm effectively reduces the artifacts and blurs, and yields better quality of reconstruction image than that with FBP algorithm in terms of both accuracy and robustness to noise.

Solution with adjustable sparsity to tomographic imaging of Cerenkov photons is presented in this work. The sparsity of radionuclides' distribution in tissues is an objective but unknown fact, and the inverse model of qualitative data is an ill-posed problem. Based on the optimization technique, the uniqueness of numerical solution to the ill-conditioned compact operator can be guaranteed by use of sparse regularization with the approximate message-passing (AMP) method. After absorbing formulations with the AMP, we analyzed the behavior of the hard thresholding operator. Iteratively numerical solutions were used to approximate the real light source by assuming the number of non-zero solution in manual mode. This modified AMP algorithm was performed in numerical simulation and physical experiments with 2-[18F]fluoro-2-deoxy-D-glucose. Experimental results indicated that the proposed method was a kind of low-complexity iterative thresholding algorithms for reconstructing 3D sparse distribution from a small set of optical measurements.

structural and functional imaging. Image reconstruction of PAI requires the solution of an inverse source problem, where the source represents the optical energy absorption distribution in the object. PAI in spherical or circular geometry gives good image resolution yet is slow in signal acquisition and image formation. Reducing the number of detection angles can ameliorate such issues. Besides, it is almost impossible to cover the entire surface of tissue. This will restrict it in the medical application with incomplete projection data. To resolve such limiting factors, in this thesis, a preconditioned conjugate gradient method is applied to the normal equations (PCCGNR method) for reconstructing the absorption distribution. Under the common assumption, a zero-mean Gaussian noise is added to the projection signals and a computer simulated has been used for the evaluation. This algorithm works well in rectification of the measurement and converges quickly onto an accurate estimate of the distribution of absolute absorption. It not only runs much faster than the FBP algorithm, but also shows stronger robustness in that it provides better image quality with detection data. We observed that diagonal preconditioners offer some improvement in convergence rate for image reconstruction, and reconstructed image preconditioning with = 0 (diagonal scaling) is closer to the true image than with > 0 . In addition, a physical experiment that will be done with our experiment equipment system further demonstrates the potential of the proposed algorithm in practical applications.

As a new modality of molecular imaging, bioluminescence imaging has been widely used in tumor detection and drug evaluation. However, BLI cannot present the depth of information for internal diseases such as a liver tumor in situ or a lung tumor in situ. In this paper, we describe a bioluminescence tomography (BLT) method based on the bioluminescent intensity attenuation calibration and applied it to the early detection of liver cancer in situ. In comparison with BLT without calibration, this method could improve the reconstruction accuracy by more than 10%. In comparison with micro-computed tomography and other traditional imaging modalities, this method can detect a liver tumor at a very early stage and provide reliable location information.

Among many optical molecular imaging modalities, bioluminescence imaging (BLI) has more and more wide application in tumor detection and evaluation of pharmacodynamics, toxicity, pharmacokinetics because of its noninvasive molecular and cellular level detection ability, high sensitivity and low cost in comparison with other imaging technologies. However, BLI can not present the accurate location and intensity of the inner bioluminescence sources such as in the bone, liver or lung etc. Bioluminescent tomography (BLT) shows its advantage in determining the bioluminescence source distribution inside a small animal or phantom. Considering the deficiency of two-dimensional imaging modality, we developed three-dimensional tomography to reconstruct the information of the bioluminescence source distribution in transgenic mOC-Luc mice bone with the boundary measured data. In this paper, to study the osteocalcin (OC) accumulation in transgenic mOC-Luc mice bone, a BLT reconstruction method based on multilevel adaptive finite element (FEM) algorithm was used for localizing and quantifying multi bioluminescence sources. Optical and anatomical information of the tissues are incorporated as a priori knowledge in this method, which can reduce the ill-posedness of BLT. The data was acquired by the dual modality BLT and Micro CT prototype system that was developed by us. Through temperature control and absolute intensity calibration, a relative accurate intensity can be calculated. The location of the OC accumulation was reconstructed, which was coherent with the principle of bone differentiation. This result also was testified by ex vivo experiment in the black 96-plate well using the BLI system and the chemiluminescence apparatus.

As a newly emerged optical imaging method, fluorescence molecular imaging technique has been receiving increasing attention for its ability of non-invasive visualization of the cellular and molecular activities. However, as a kind of background noise, autofluorescence is a major disturbing factor in fluorescence molecular imaging. In this paper, we proposed a novel method to eliminate autofluorescence of small animals. The method is based on the fact that most autofluorescent signal has a broad excitation and emission spectrum, whereas specific fluorescent probe has a narrow one. First, two fluorescent images are obtained at two different excitation wavelengths. Then we divide the two obtained fluorescent images into blocks with the size of 8×8 pixel. The two blocks from the same position of the two different images respectively constitute a block pair. The ratio of one block's summation of total pixel value to that of ther other block belonging to the same block pair is calculated. After that, we classify all block pairs into fluorescent and nonfluorescent ones by ratio. The former are considered to be actual fluorescent regions. In next step, we adopt an adaptive cluster analysis method to classify all fluorescent block pairs into multiple interest regions. A general centroid algorithm is then applied to locate the center of each interest regions. We recover the fluorescent interest regions using flood filling algorithm. Finally, we choose a GFP-transfected tumor mouse model and a GFP-transplanted mouse skin model to validate our algorithm.

Fluorescence molecular tomography (FMT) has become a promising imaging modality for in vivo small animal molecular imaging, and has many successful applications. This is partly due to the wealth of the fluorescent probes. By labeling the regions of interest with fluorescent probes, FMT can achieve non-invasive investigation of the biological process by localizing the targeted probes based on certain inverse mathematical models. However, FMT is usually an illposed problem, and some form of regularization should be included to stabilize the problem, which can be considered as the a priori information of the fluorescent probe bio-distribution. When FMT is used for the early detection of tumors, an important characteristic is the sparsity of the fluorescent sources. This is because tumors are usually very small and sparse at this stage. Considering this, general sparsity-promoting Lp-norm regularization is utilized in this paper. The iterated shrinkage based reconstruction method is adopted to solve the general Lp regularization problem. However, the original iterated shrinkage method is proved to have a linear convergence rate, and a large number of iterations are needed to obtain satisfactory results. In this paper, an improved iterated shrinkage based FMT reconstruction algorithm is proposed. By using the solutions from two previous iterations to determine the current solution, the convergence rate can be greatly increased. Heterogeneous simulation experiment shows that the proposed method can obtain comparable results with greatly reduced number of iterations compared with the original iterated shrinkage based method, which makes it a practical reconstruction algorithm.

Fluorescence molecular tomography (FMT) is a promising technique for in vivo small animal imaging. In this paper, the sparsity of the fluorescent sources is considered as the a priori information and is promoted by incorporating L1 regularization. Then a reconstruction algorithm based on stagewise orthogonal matching pursuit is proposed, which treats the FMT problem as the basis pursuit problem. To evaluate this method, we compare it to the iterated-shrinkage-based algorithm with L1 regularization. Numerical simulations and physical experiments show that the proposed method can obtain comparable or even slightly better results. More importantly, the proposed method was at least 2 orders of magnitude faster in these experiments, which makes it a practical reconstruction algorithm.

Since the 1960s, the stem cells have been extensively studied including embryonic stem cells, neural stem cells, bone marrow hematopoietic stem cells, and mesenchymal stem cells. In the recent years, several stem cells have been initially used in the treatment of diseases, such as in bone marrow transplant. At the same time, isolation and culture experimental technologies for stem cell research have been widely developed in recent years. In addition, molecular imaging technologies including optical molecular imaging, positron emission tomography, single-photon emission computed tomography, and computed tomography have been developed rapidly in recent the 10 years and have also been used in the research on disease mechanism and evaluation of treatment of disease related with stem cells. This paper will focus on recent typical isolation, culture, and observation techniques of stem cells followed by a concise introduction. Finally, the current challenges and the future applications of the new technologies in stem cells are given according to the understanding of the authors, and the paper is then concluded.

Bioluminescence tomography (BLT) is a novel optical molecular imaging (MI) modality. It can reconstruct the inner bioluminescent light source distribution, according to the surface light distribution. The trust region method (TRM) can overcome the ill-posedness of BLT for its regularization property. As there exists a "TRUST" function that can solve the trust region subproblem in Matlab and Matlab's powerful matrix operation ability suited for TRM, the TRM is implemented in Matlab. Then the Matlab code of TRM is transformed into a dynamic linked library (DDL) and mixed together with the C++ code of the adaptive finite element (AFE) framework, using the mixed-language programming technology (MLPT). There are two main advantages of the MLPT. The first is taking advantages of all the participated programming languages. The second is time efficient. The usual way of transferring data between programmes written in different programming languages is to write the data first into files that are stored in the hard discs in one programme, and then read the files from another programme. Besides wasting time on writing and reading, it is difficult to keep the precision of the data. The DLL based MLPT can eliminate the need of installing code compilers in the platform running the software. Furthermore, in DLL, the code is implemented in C/C++ with high time efficiency, while the code in Matlab remains relatively low time efficiency. Finally, a numerical experiment is carried out to show MLPT's usage in the source reconstruction procedure of BLT, using the MLPT based on DLL.