Projected algebraic reconstruction technique-network for high-fidelity diffuse fluorescence tomography reconstruction.

We propose a model-driven projected algebraic reconstruction technique (PART)-network (PART-Net) that leverages the advantages of the traditional model-based method and the neural network to improve the imaging quality of diffuse fluorescence tomography. In this algorithm, nonnegative prior information is incorporated into the ART iteration process to better guide the optimization process, and thereby improve imaging quality. On this basis, PART in conjunction with a residual convolutional neural network is further proposed to obtain high-fidelity image reconstruction. The numerical simulation results demonstrate that the PART-Net algorithm effectively improves noise robustness and reconstruction accuracy by at least 1-2 times and exhibits superiority in spatial resolution and quantification, especially for a small-sized target (r=2m m), compared with the traditional ART algorithm. Furthermore, the phantom and in vivo experiments verify the effectiveness of the PART-Net, suggesting strong generalization capability and a great potential for practical applications.

Performance enhancement of diffuse fluorescence tomography based on an extended Kalman filtering-long short term memory neural network correction model.

To alleviate the ill-posedness of diffuse fluorescence tomography (DFT) reconstruction and improve imaging quality and speed, a model-derived deep-learning method is proposed by combining extended Kalman filtering (EKF) with a long short term memory (LSTM) neural network, where the iterative process parameters acquired by implementing semi-iteration EKF (SEKF) served as inputs to the LSTM neural network correction model for predicting the optimal fluorescence distributions. To verify the effectiveness of the SEKF-LSTM algorithm, a series of numerical simulations, phantom and in vivo experiments are conducted, and the experimental results are quantitatively evaluated and compared with the traditional EKF algorithm. The simulation experimental results show that the proposed new algorithm can effectively improve the reconstructed image quality and reconstruction speed. Importantly, the LSTM correction model trained by the simulation data also obtains satisfactory results in the experimental data, suggesting that the SEKF-LSTM algorithm possesses strong generalization ability and great potential for practical applications.

IR780-based diffuse fluorescence tomography for cancer detection.

IR780 iodide is a commercially available targeted near-infrared contrast agent for in vivo imaging and cancer photodynamic or photothermal therapy, whereas the accumulation, dynamics, and retention of IR780 in biological tissue, especially in tumor is still under-explored. Diffuse fluorescence tomography (DFT) can be used for localization and quantification of the three-dimensional distribution of NIR fluorophores. Herein, a homemade DFT imaging system combined with tumor-targeted IR780 was utilized for cancer imaging and pharmacokinetic evaluation. The aim of this study is to comprehensively assess the biochemical and pharmacokinetic characteristics of IR780 with the aid of DFT imaging. The optimal IR780 concentration (20 µg/mL) was achieved first. Subsequently, the good biocompatibility and cellar uptake of IR780 was demonstrated through the mouse acute toxic test and cell assay. In vivo, DFT imaging effectively identified various subcutaneous tumors and revealed the long-term retention of IR780 in tumors and rapid metabolism in the liver. Ex vivo imaging indicated IR780 was mainly concentrated in tumor and lung with significantly different from the distribution in other organs. DFT imaging allowed sensitive tumor detection and pharmacokinetic rates analysis. Simultaneously, the kinetics of IR780 in tumors and liver provided more valuable information for application and development of IR780.

Effect of PhenylEthanol Glycosides from Cistanche Tubulosa on Autophagy and Apoptosis in H22 Tumor-Bearing Mice.

An effectual remedy for hepatocellular carcinoma (HCC) and knowledge of the mechanism are urgently needed. Researchers have found that CPhGs, an extract from Cistanche tubulosa (Schenk) Wight, had better antitumor effects, but its mechanism is still unknown. In the present study, using an H22 tumor-bearing mouse as a model, we investigated the antitumor effects of CPhGs and the effect of CPhGs on autophagy and apoptosis. Besides, we also discussed the role of autophagy with the help of HCQ and rapamycin. Our results show that CPhGs inhibit tumor growth and induce apoptosis and autophagy of tumor tissue. TUNEL staining displayed that tumor apoptosis rate increased after the intervention of CPhGs, and immunohistochemistry and western blot showed that cleaved-PARP and cleaved-caspase 3 were upregulated after the intervention of CPhGs, and these results were most pronounced in the high-dose group. Autophagy results revealed that CPhGs increased the number of autophagosomes, increased the level of LC3B-II, and decreased the level of p62. Finally, our results showed that excessive autophagy suppresses tumor growth, whereas inhibition of autophagy does the opposite, which indicated that CPhGs induced autophagic death in H22 hepatoma-bearing mice. These data altogether confirmed the involvement of apoptosis and autophagy in CPhGs treatment for HCC.

Targeting delivery of synergistic dual drugs with elastic PEG-modified multi-functional nanoparticles for hepatocellular carcinoma therapy.

Integration of multiple advantages in one system has been leveraged to overcome multiple biological barriers in anti-tumor therapeutic strategies. In this study, multi-functional nanoparticles (MFNPs) are constructed by layer-by-layer method. MFNPs are modified with pH-responsive elastic PEG-GPC3MAb (glypican-3 monoclonal antibody), which draws back into PEG layer in blood and normal tissues; and stretches out of MFNPs surface in the acidic tumor microenvironment. It is proved that blank MFNPs have good biocompatibility by MTT and acute toxicity assays. Elastic PEG chains are able to respond sensitively in different pH environments (6.8 and 7.4), which is demonstrated by transmission electron microscope (TEM) and 1H nuclear magnetic resonance (1H NMR). In vitro experiments show that MFNPs have better specificity to Hepa 1-6 cells, can escape from lysosomes, and are able to increase the nuclear delivery of dual drugs for synergistic therapy, which are proved by flow cytometry, MTT, confocal laser scanning microscopy, and western blot studies. In vivo experiments indicate that MFNPs show extending circulation half-life in blood, promoting localization into tumor tissues, improving the therapeutic efficacy of BAL b/c nude mice with subcutaneous tumors. Overall, the results indicate that FMNPs are a potential candidate for hepatocellular carcinoma therapy.