Silver nanoparticles induced cytotoxicity in HT22 cells through autophagy and apoptosis via PI3K/AKT/mTOR signaling pathway.

With the widespread application and inevitable environmental exposure, silver nanoparticles (AgNPs) can be accumulated in various organs. More serious concerns are raised on the biological safety and potential toxicity of AgNPs in the central nervous system (CNS), especially in the hippocampus. This study aimed to investigate the biological effects and the role of PI3K/AKT/mTOR signaling pathway in AgNPs mediated cytotoxicity using the mouse hippocampal neuronal cell line (HT22 cells). AgNPs reduced cell viability and induced membrane leakage in a dose-dependent manner, determined by the MTT and LDH assay. In doses of 25, 50, 100 µg mL-1 for 24 h, AgNPs promoted the excessive production of reactive oxygen species (ROS) and caused the oxidative stress in HT22 cells. AgNPs induced autophagy, determined by the transmission electron microscopy observation, upregulation of LC3 II/I and downregulation of p62 expression levels. The mechanistic investigation showed that the PI3K/AKT/mTOR signaling pathway was activated by phosphorylation, which was enrolled in an AgNP-induced autophagy process. AgNPs could further trigger the apoptosis by upregulation of caspase-3 and Bax and downregulation of Bcl-2 in HT22 cells. These results revealed AgNP-induced cytotoxicity in HT22 cells, which was mediated by autophagy and apoptosis via the PI3K/AKT/mTOR signaling pathway. The study could provide the experimental evidence and explanation for the potential neurotoxicity triggered by AgNPs in vitro.

Biodistribution and organ oxidative damage following 28 days oral administration of nanosilver with/without coating in mice.

This study evaluated the biodistribution and organ oxidative effects of silver nanoparticles (AgNPs) coated with/without polyvinylpyrrolidone (PVP) (AgNP-20 and AgNP-PVP) in mice; these were administered by gavage at a dose of 10-250 mg/kg body weight per day for 28 days. The results showed that both the AgNPs could induce subacute toxicity and oxidative damage to mice and were mainly accumulated in the liver and spleen and excreted by feces. AgNPs could be absorbed into blood and might cross the blood-brain barrier, and be distributed extensively in mice. The malondialdehyde content in the liver, lungs and kidneys increased in both AgNP groups, while the content of glutathione decreased, and the activity of superoxide dismutase increased at first and then decreased along with the increased doses. Inflammatory pathological changes in the lung and liver at high dose of both AgNPs were consistent with increases in glutamate pyruvic transaminase, glutamate oxaloacetic transaminase and the total protein in serum detection. The Ag content was detected in organs, with the highest content in the liver, followed by spleen, while the Ag content in feces was about 500 times higher than that in urine. AgNP-PVP could induce higher oxidative stress and subacute toxicity than AgNP-20 at the same dose, which might be related to the higher concentrations and more Ag+ ions released in mice after AgNP-PVP exposure. The data from this research provided information on toxicity and biodistribution of AgNPs following gavage administration in mice, and might shed light for future application of AgNPs in daily life.

Genotoxic effects of silver nanoparticles with/without coating in human liver HepG2 cells and in mice.

With the rapid expansion of human exposure to silver nanoparticles (AgNPs), the genotoxicity screening is critical to the biosafety evaluation of nanosilver. This study assessed DNA damage and chromosomal aberration in the human hepatoma cell line (HepG2) as well as the effects on the micronucleus of bone marrow in mice induced by 20 nm polyvinylpyrrolidone-coated nanosilver (PVP-AgNPs) and 20 nm bare nanosilver (AgNPs). Our results showed that the two types of AgNPs, in doses of 20-160 µg/mL, could cause genetic toxicological changes on HepG2 cells. The DNA damage degree of HepG2 cells in 20 nm AgNPs was higher than that in 20 nm PVP-AgNPs, while the 20 nm PVP-AgNPs caused more serious chromosomal aberration than 20 nm AgNPs. Both kinds of AgNPs caused genetic toxicity in a dose-dependent manner in HepG2 cells. In the micronucleus test on mouse bone marrow cells, in doses of 10, 50 and 250 mg/kg body weight administered orally for 28 days once a day, the two kinds of AgNPs have no obvious inhibitory effect on the mouse bone marrow cells, and the effect of chromosome aberration could be documented at the high dose of 250 mg/kg. These results suggest that AgNPs have genotoxic effects in HepG2 cells and limited effects on bone marrow in mice; both in vitro and in vivo tests could be of great importance on the evaluation of genotoxicity of nanosilver. These findings can provide useful toxicological information that can help to assess genetic toxicity of nanosilver in vitro and in vivo.

Application Research for Fusion Model of Pseudolabel and Cross Network.

Datasets usually suffer from supervised information missing and weak generalization ability in deep convolution neural network. In this paper, pseudolabel (PL) of Weakly Supervised Learning (WSL) was used to address the problem of supervised information missing, while Cross Network (CN) of Multitask Learning (MTL) was used to solve the problem of weak generalization ability in deep convolution neural network. In PL, the data of supervised information missing was predicted; thus, PL of the corresponding data was generated. In CN, PL data and labeled data were taken as two tasks to train together. Firstly, the labeled data was divided into training dataset and testing dataset, respectively, and image preprocessing was carried out. Secondly, the network was initialized and trained, and the model with high accuracy and good generalization was selected as the optimal model. Then, the optimal model was used to predict the unlabeled data and generate PL. Finally, the steps above were repeated several times to find a better optimal model. In the experiments of the fusion model of PL and CN, Facial Beauty Prediction was regarded as main task and the others as auxiliary tasks. Experimental results show that the model was suitable for multitask training of different tasks in different or similar datasets, and the accuracy of the main task of Facial Beauty Prediction reaches 64.76%, higher than the highest accuracy by conventional methods.

Silver nanoparticles modulate mitochondrial dynamics and biogenesis in HepG2 cells.

Silver nanoparticles (AgNPs) are inevitably released into the environment owing to their widespread applications in industry and medicine. The potential of their toxicity has aroused a great concern. Previous studies have shown that AgNPs exposure in HepG2 cells is primarily related to the damage of mitochondria, which includes induction of mitochondrial swelling and increase of intracellular levels of reactive oxygen species (ROS), the collapse of mitochondrial membrane potential and induction of apoptosis through a mitochondrial pathway. In this study, the effects of AgNPs exposure in HepG2 cells on mitochondrial dynamics and biogenesis were investigated. AgNPs were found to induce mitochondrial morphological and structural alterations. The expressions of key proteins (Drp1, Fis1, OPA1, Mff, Mfn1, and Mfn2) related to mitochondrial fission/fusion event were changed. Especially the expression of fission-related protein 1 (p-Drp1) (Ser616) was significantly up-regulated, whereas the expression of mitochondrial biogenesis protein (PGC-1α) was reduced in AgNP-treated cells. Concomitantly, the expression of autophagy marker proteins (LC3B and p62) was increased. The results suggested that AgNPs could trigger cytotoxicity by targeting the mitochondria, resulting in the disruption of mitochondrial function, damage to the mitochondrial structure and morphology, interfering in mitochondrial dynamics and biogenesis. The mitochondria could be a critical target of AgNPs in cells. The functions of mitochondria could be used for assessing the cytotoxic effects associated with AgNPs in cells.

BeautyNet: Joint Multiscale CNN and Transfer Learning Method for Unconstrained Facial Beauty Prediction.

Because of the lack of discriminative face representations and scarcity of labeled training data, facial beauty prediction (FBP), which aims at assessing facial attractiveness automatically, has become a challenging pattern recognition problem. Inspired by recent promising work on fine-grained image classification using the multiscale architecture to extend the diversity of deep features, BeautyNet for unconstrained facial beauty prediction is proposed in this paper. Firstly, a multiscale network is adopted to improve the discriminative of face features. Secondly, to alleviate the computational burden of the multiscale architecture, MFM (max-feature-map) is utilized as an activation function which can not only lighten the network and speed network convergence but also benefit the performance. Finally, transfer learning strategy is introduced here to mitigate the overfitting phenomenon which is caused by the scarcity of labeled facial beauty samples and improves the proposed BeautyNet's performance. Extensive experiments performed on LSFBD demonstrate that the proposed scheme outperforms the state-of-the-art methods, which can achieve 67.48% classification accuracy.

Robust SAR Automatic Target Recognition Based on Transferred MS-CNN with L2-Regularization.

Though Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) via Convolutional Neural Networks (CNNs) has made huge progress toward deep learning, some key issues still remain unsolved due to the lack of sufficient samples and robust model. In this paper, we proposed an efficient transferred Max-Slice CNN (MS-CNN) with L2-Regularization for SAR ATR, which could enrich the features and recognize the targets with superior performance. Firstly, the data amplification method is presented to reduce the computational time and enrich the raw features of SAR targets. Secondly, the proposed MS-CNN framework with L2-Regularization is trained to extract robust features, in which the L2-Regularization is incorporated to avoid the overfitting phenomenon and further optimizing our proposed model. Thirdly, transfer learning is introduced to enhance the feature representation and discrimination, which could boost the performance and robustness of the proposed model on small samples. Finally, various activation functions and dropout strategies are evaluated for further improving recognition performance. Extensive experiments demonstrated that our proposed method could not only outperform other state-of-the-art methods on the public and extended MSTAR dataset but also obtain good performance on the random small datasets.

Deep Convolutional Neural Network Used in Single Sample per Person Face Recognition.

Face recognition (FR) with single sample per person (SSPP) is a challenge in computer vision. Since there is only one sample to be trained, it makes facial variation such as pose, illumination, and disguise difficult to be predicted. To overcome this problem, this paper proposes a scheme combined traditional and deep learning (TDL) method to process the task. First, it proposes an expanding sample method based on traditional approach. Compared with other expanding sample methods, the method can be used easily and conveniently. Besides, it can generate samples such as disguise, expression, and mixed variation. Second, it uses transfer learning and introduces a well-trained deep convolutional neural network (DCNN) model and then selects some expanding samples to fine-tune the DCNN model. Third, the fine-tuned model is used to implement experiment. Experimental results on AR face database, Extend Yale B face database, FERET face database, and LFW database demonstrate that TDL achieves the state-of-the-art performance in SSPP FR.