Lipid droplets' functional protein caveolin-2 is associated with lipid metabolism-related molecule FABP5 and EMT marker E-cadherin in oral epithelial dysplasia.

AIMS: To explore the accumulation of lipid droplets (LDs) and its relationship with lipid metabolism, and epithelial-mesenchymal transition (EMT) in the carcinogenesis processes in the oral cavity. METHODS: LDs were stained by oil red O. Forty-eight oral squamous cell carcinomas (OSCC), 78 oral potentially malignant disorders (OPMDs) and 25 normal tissue sections were included to explore the LDs surface protein caveolin-2 and perilipin-3, lipid metabolism-related molecule FABP5 and EMT biomarker E-cadherin expression by immunohistochemical staining. RESULTS: The accumulation of LDs was observed in OPMDs and OSCCs compared with normal tissues (p<0.05). In general, an increasing trend of caveolin-2, perilipin-3 and FABP5 expression was detected from the normal to OPMDs to OSCC groups (p<0.05). Additionally, caveolin-2, perilipin-3 and FABP5 expression were positively correlated with epithelial dysplasia in OPMDs, whereas E-cadherin positivity was negatively correlated with histopathological grade in both OPMDs and OSCC, respectively. A negative correlation of caveolin-2 (p<0.01, r =-0.1739), and FABP5 (p<0.01, r =-0.1880) with E-cadherin expression was detected. The caveolin-2 (p<0.0001, r=0.2641) and perilipin-3 (p<0.05, r=0.1408) staining was positively correlated with FABP5. Increased caveolin-2 expression was related to local recurrence and worse disease-free survival (p<0.05). CONCLUSION: In the oral epithelial carcinogenesis process, LDs begin to accumulate early in the precancerous stage. LDs may be the regulator of FABP5-associated lipid metabolism and may closely related to the process of EMT; caveolin-2 could be the main functional protein.

The Potential of Lipid Droplet-associated Genes as Diagnostic and Prognostic Biomarkers in Head and Neck Squamous Cell Carcinoma.

OBJECTIVE: The role of lipid droplets (LDs) and lipid droplet-associated genes (LD-AGs) remains unclear in head and neck squamous cell carcinoma (HNSCC). This study aimed to investigate LDs in HNSCC and identify LD-AGs essential for the diagnosis and prognosis of HNSCC patients. METHODS: The LDs in the HNSCC and normal cell lines were stained with oil red O. Bioinformatic analysis was used to find LD-AGs in HNSCC that had diagnostic and prognostic significance. RESULTS: LDs accumulation was increased in HNSCC cell lines compared with normal cell lines (P<0.05). Fifty-three differentially expressed genes, including 34 upregulated and 19 downregulated, were found in HNSCC based on the TCGA platform (P<0.05). Then, 53 genes were proved to be functionally enriched in lipid metabolism and LDs. Among them, with an AUC value > 0.7, 34 genes demonstrated a high predictive power. Six genes (AUP1, CAV1, CAV2, CAVIN1, HILPDA, and SQLE) out of 34 diagnostic genes were linked to overall survival in patients with HNSCC (P<0.05). The significant prognostic factors AUP1, CAV1, CAV2, and SQLE were further identified using the univariate and multivariate cox proportional hazard models (P<0.05). The protein expression of CAV2 and SQLE was significantly increased in the HNSCC tissue compared to normal tissues (P<0.05). Finally, the knockdown of the four LD-AGs decreased LDs accumulation, respectively. CONCLUSIONS: Increased LDs accumulation was a hallmark of HNSCC, and AUP1, CAV1, CAV2, and SQLE were discovered as differentially expressed LD-AGs with diagnostic and prognostic potential in HNSCC.

Parameter-Free State Estimation Based on Kalman Filter with Attention Learning for GPS Tracking in Autonomous Driving System.

GPS-based maneuvering target localization and tracking is a crucial aspect of autonomous driving and is widely used in navigation, transportation, autonomous vehicles, and other fields.The classical tracking approach employs a Kalman filter with precise system parameters to estimate the state. However, it is difficult to model their uncertainty because of the complex motion of maneuvering targets and the unknown sensor characteristics. Furthermore, GPS data often involve unknown color noise, making it challenging to obtain accurate system parameters, which can degrade the performance of the classical methods. To address these issues, we present a state estimation method based on the Kalman filter that does not require predefined parameters but instead uses attention learning. We use a transformer encoder with a long short-term memory (LSTM) network to extract dynamic characteristics, and estimate the system model parameters online using the expectation maximization (EM) algorithm, based on the output of the attention learning module. Finally, the Kalman filter computes the dynamic state estimates using the parameters of the learned system, dynamics, and measurement characteristics. Based on GPS simulation data and the Geolife Beijing vehicle GPS trajectory dataset, the experimental results demonstrated that our method outperformed classical and pure model-free network estimation approaches in estimation accuracy, providing an effective solution for practical maneuvering-target tracking applications.

Location estimation based on feature mode matching with deep network models.

Introduction: Global navigation satellite system (GNSS) signals can be lost in viaducts, urban canyons, and tunnel environments. It has been a significant challenge to achieve the accurate location of pedestrians during Global Positioning System (GPS) signal outages. This paper proposes a location estimation only with inertial measurements. Methods: A method is designed based on deep network models with feature mode matching. First, a framework is designed to extract the features of inertial measurements and match them with deep networks. Second, feature extraction and classification methods are investigated to achieve mode partitioning and to lay the foundation for checking different deep networks. Third, typical deep network models are analyzed to match various features. The selected models can be trained for different modes of inertial measurements to obtain localization information. The experiments are performed with the inertial mileage dataset from Oxford University. Results and discussion: The results demonstrate that the appropriate networks based on different feature modes have more accurate position estimation, which can improve the localization accuracy of pedestrians in GPS signal outages.

Deep Spatio-Temporal Graph Network with Self-Optimization for Air Quality Prediction.

The environment and development are major issues of general concern. After much suffering from the harm of environmental pollution, human beings began to pay attention to environmental protection and started to carry out pollutant prediction research. A large number of air pollutant predictions have tried to predict pollutants by revealing their evolution patterns, emphasizing the fitting analysis of time series but ignoring the spatial transmission effect of adjacent areas, leading to low prediction accuracy. To solve this problem, we propose a time series prediction network with the self-optimization ability of a spatio-temporal graph neural network (BGGRU) to mine the changing pattern of the time series and the spatial propagation effect. The proposed network includes spatial and temporal modules. The spatial module uses a graph sampling and aggregation network (GraphSAGE) in order to extract the spatial information of the data. The temporal module uses a Bayesian graph gated recurrent unit (BGraphGRU), which applies a graph network to the gated recurrent unit (GRU) so as to fit the data's temporal information. In addition, this study used Bayesian optimization to solve the problem of the model's inaccuracy caused by inappropriate hyperparameters of the model. The high accuracy of the proposed method was verified by the actual PM2.5 data of Beijing, China, which provided an effective method for predicting the PM2.5 concentration.

Novel quorum sensing inhibitor Echinatin as an antibacterial synergist against Escherichia coli.

A new antibacterial strategy based on inhibiting bacterial quorum sensing (QS) has emerged as a promising method of attenuating bacterial pathogenicity and preventing bacterial resistance to antibiotics. In this study, we screened Echinatin (Ech) with high-efficiency anti-QS from 13 flavonoids through the AI-2 bioluminescence assay. Additionally, crystal violet (CV) staining combined with confocal laser scanning microscopy (CLSM) was used to evaluate the effect of anti-biofilm against Escherichia coli (E. coli). Further, the antibacterial synergistic effect of Ech and marketed antibiotics were measured by broth dilution and Alamar Blue Assay. It was found that Ech interfered with the phenotype of QS, including biofilm formation, exopolysaccharide (EPS) production, and motility, without affecting bacterial growth and metabolic activity. Moreover, qRT-PCR exhibited that Ech significantly reduced the expression of QS-regulated genes (luxS, pfs, lsrB, lsrK, lsrR, flhC, flhD, fliC, csgD, and stx2). More important, Ech with currently marketed colistin antibiotics (including colistin B and colistin E) showed significantly synergistically increased antibacterial activity in overcoming antibiotic resistance of E. coli. In summary, these results suggested the potent anti-QS and novel antibacterial synergist candidate of Ech for treating E. coli infections.

A Variational Bayesian Deep Network with Data Self-Screening Layer for Massive Time-Series Data Forecasting.

Compared with mechanism-based modeling methods, data-driven modeling based on big data has become a popular research field in recent years because of its applicability. However, it is not always better to have more data when building a forecasting model in practical areas. Due to the noise and conflict, redundancy, and inconsistency of big time-series data, the forecasting accuracy may reduce on the contrary. This paper proposes a deep network by selecting and understanding data to improve performance. Firstly, a data self-screening layer (DSSL) with a maximal information distance coefficient (MIDC) is designed to filter input data with high correlation and low redundancy; then, a variational Bayesian gated recurrent unit (VBGRU) is used to improve the anti-noise ability and robustness of the model. Beijing's air quality and meteorological data are conducted in a verification experiment of 24 h PM2.5 concentration forecasting, proving that the proposed model is superior to other models in accuracy.

Glaucocalyxin A impairs tumor growth via amplification of the ATF4/CHOP/CHAC1 cascade in human oral squamous cell carcinoma.

ETHNOPHARMACOLOGICAL RELEVANCE: The natural extract glaucocalyxin A (GLA), purified from the aboveground sections of the Chinese traditional medicinal herb Rabdosia japonica (Burm. f.) Hara var. glaucocalyx (Maxim.) Hara, has various pharmacological benefits, such as anti-bacterial, anti-coagulative, anti-neoplastic, and anti-inflammatory activities. Although GLA has shown anti-tumor activity against various cancers, the therapeutic potential and biological mechanisms of GLA remain to be further explored in oral squamous cell carcinoma (OSCC). AIM OF THE STUDY: This study aimed to elucidate the therapeutic potential and regulatory mechanisms of GLA in OSCC. MATERIALS AND METHODS: The cell proliferation and apoptosis effects of GLA were analyzed by CCK-8, clone formation, Annexin V/PI staining, and apoptotic protein expression in vitro. An OSCC xenograft model was applied to confirm the anti-neoplastic effect in vivo. Furthermore, the changes of reactive oxygen species (ROS) were determined by DCFH-DA probe and GSH/GSSG assay, and inhibited by the pan-caspase inhibitor Z-VAD(OMe)-FMK and the ROS scavenger N-acetylcysteine (NAC). The modulation of GLA on mitochondria and ER-dependent apoptosis pathways was analyzed by JC-1 probe, quantitative real-time PCR, and Western blot. Finally, public databases, clinical samples, and transfection cells were analyzed to explore the importance of GLA's indirect targeting molecule CHAC1 in OSCC. RESULTS: GLA significantly inhibited cell proliferation and induced apoptosis in vitro and in vivo. GLA perturbed the redox homeostasis, and cell apoptosis was totally rescued by Z-VAD(OMe)-FMK and NAC. Furthermore, GLA activated the mitochondrial apoptosis pathway. Simultaneously, the overexpression and knockdown of CHAC1 dramatically affected GLA-mediated apoptosis. The endoplasmic reticulum stress-associated ATF4/CHOP signal was identified to participate in GLA-upregulated CHAC1 expression. Finally, we found that CHAC1 expression was lower in OSCC compared with normal tissues and positively correlated with 4-Hydroxynonenal (4-HNE) level. High CHAC1 expression also indicated better overall survival. Moreover, CHAC1 selectively regulated the viability of oral cancer cells. CONCLUSION: GLA is a promising therapeutic agent that activates the ROS-mediated ATF4/CHOP/CHAC1 axis in OSCC patients.

Nanomedicines in oral cancer: inspiration comes from extracellular vesicles and biomimetic nanoparticles.

Oral cancer is a common life-threatening malignancy having high mortality and morbidity rates. During the treatment process, individuals unavoidably experience severe side effects. It is essential to develop safer and more effective strategies. Currently, extracellular vesicles (EVs) and biomimetic nanoparticles are nanomedicines with long-term blood circulation and lower off-target toxicity that orchestrate immune responses and accumulate specifically in tumor sites. EVs create a synergetic effect by encapsulating drugs and collaborating with naturally loaded elements in the EVs. Biomimetic nanoparticles retain the characteristic features of the synthetic nanocarriers and inherit the intrinsic cell membrane functionalities. This review outlines the properties, applications, challenges, pros and cons of EVs and biomimetic nanoparticles, providing novel perspectives on oral cancer.

This review explains how extracellular vesicles (EVs) and biomimetic nanoparticles are emerging as nanomedicines applied in oral cancer. EVs are phospholipid bilayer vesicles, mainly including exosomes and microvesicles, responsible for intercellular communication and cargo transport. EVs can carry RNA, metabolites and other molecular payloads. Biomimetic nanomedicines are synthetic nanoparticles coated with the parent or host cell membrane to escape the immune system and elevate targeting ability. Various cell membranes have been used for camouflaging nanoparticles, such as red blood cells, white blood cells, platelets, mesenchymal stem cells and cancer cell membranes. During the treatment process, individuals unavoidably experience severe side effects. It is essential to develop safer and more effective strategies. Currently, EVs and biomimetic nanoparticles are nanomedicines with long-term blood circulation and lower off-target toxicity that orchestrate immune responses and accumulate specifically in tumor sites. EVs create a synergetic effect by encapsulating drugs and collaborating with naturally loaded elements in the EVs. Biomimetic nanoparticles retain the characteristic features of the synthetic nanocarriers and inherit the intrinsic cell membrane functionalities. This review outlines the properties, applications, challenges, pros and cons of EVs and biomimetic nanoparticles, providing novel perspectives on oral cancer.

Modeling and Analysis of Data-Driven Systems through Computational Neuroscience Wavelet-Deep Optimized Model for Nonlinear Multicomponent Data Forecasting.

Complex time series data exists widely in actual systems, and its forecasting has great practical significance. Simultaneously, the classical linear model cannot obtain satisfactory performance due to nonlinearity and multicomponent characteristics. Based on the data-driven mechanism, this paper proposes a deep learning method coupled with Bayesian optimization based on wavelet decomposition to model the time series data and forecasting its trend. Firstly, the data is decomposed by wavelet transform to reduce the complexity of the time series data. The Gated Recurrent Unit (GRU) network is trained as a submodel for each decomposition component. The hyperparameters of wavelet decomposition and each submodel are optimized with Bayesian sequence model-based optimization (SMBO) to develop the modeling accuracy. Finally, the results of all submodels are added to obtain forecasting results. The PM2.5 data collected by the US Air Quality Monitoring Station is used for experiments. By comparing with other networks, it can be found that the proposed method outperforms well in the multisteps forecasting task for the complex time series.

The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods.

State estimation is widely used in various automated systems, including IoT systems, unmanned systems, robots, etc. In traditional state estimation, measurement data are instantaneous and processed in real time. With modern systems' development, sensors can obtain more and more signals and store them. Therefore, how to use these measurement big data to improve the performance of state estimation has become a hot research issue in this field. This paper reviews the development of state estimation and future development trends. First, we review the model-based state estimation methods, including the Kalman filter, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF), cubature Kalman filter (CKF), etc. Particle filters and Gaussian mixture filters that can handle mixed Gaussian noise are discussed, too. These methods have high requirements for models, while it is not easy to obtain accurate system models in practice. The emergence of robust filters, the interacting multiple model (IMM), and adaptive filters are also mentioned here. Secondly, the current research status of data-driven state estimation methods is introduced based on network learning. Finally, the main research results for hybrid filters obtained in recent years are summarized and discussed, which combine model-based methods and data-driven methods. This paper is based on state estimation research results and provides a more detailed overview of model-driven, data-driven, and hybrid-driven approaches. The main algorithm of each method is provided so that beginners can have a clearer understanding. Additionally, it discusses the future development trends for researchers in state estimation.

Hypoxia protects H9c2 cells against Ferroptosis through SENP1-mediated protein DeSUMOylation.

Hypoxia affects proliferation, differentiation, as well as death of cardiomyocyte, and plays an important role in the development of myocardial ischemia. However, the detailed mechanisms through which hypoxia regulates cardiomyocyte ferroptosis have not been explored. In this study, we revealed that hypoxia suppresses the proliferation, migration, and erastin-induced ferroptosis of H9c2 cells. First, we confirmed the upregulation of SENP1 in H9c2 cells cultured under hypoxic conditions. Through adenovirus-mediated SENP1 gene transfection, we demonstrated that SENP1 overexpression could enhance H9c2 cell proliferation and migration while also protecting H9c2 cells from erastin-induced ferroptosis. Furthermore, through immunoprecipitation and western blotting, we confirmed that SENP1 mediated deSUMOylation of HIF-1α and ACSL4 in H9c2 cells. In conclusion, this study describes the underlying mechanism through which hypoxia upregulates SENP1 expression, in turn protecting against ferroptosis via the regulation of HIF-1α and ACSL4 deSUMOylation. Our findings provide a theoretical foundation for the development of novel therapeutics for ischemic heart diseases.

Distributed Deep Fusion Predictor for a Multi-Sensor System Based on Causality Entropy.

Trend prediction based on sensor data in a multi-sensor system is an important topic. As the number of sensors increases, we can measure and store more and more data. However, the increase in data has not effectively improved prediction performance. This paper focuses on this problem and presents a distributed predictor that can overcome unrelated data and sensor noise: First, we define the causality entropy to calculate the measurement's causality. Then, the series causality coefficient (SCC) is proposed to select the high causal measurement as the input data. To overcome the traditional deep learning network's over-fitting to the sensor noise, the Bayesian method is used to obtain the weight distribution characteristics of the sub-predictor network. A multi-layer perceptron (MLP) is constructed as the fusion layer to fuse the results from different sub-predictors. The experiments were implemented to verify the effectiveness of the proposed method by meteorological data from Beijing. The results show that the proposed predictor can effectively model the multi-sensor system's big measurement data to improve prediction performance.

Knockdown of ectodysplasin-A receptor-associated adaptor protein exerts a tumor-suppressive effect in tongue squamous cell carcinoma cells.

Tongue squamous cell carcinoma (TSCC) is a common malignancy in oral cancer with a high mortality and morbidity. The ectodysplasin-A receptor-associated adaptor protein (EDARADD) is a death domain-containing adaptor protein that interacts with the TNF family ligand ectodysplasin A receptor. It is known that EDARADD has an effect on the development of ectodermal derivative tissues, such as hair and teeth. EDARADD expression is also associated with the development of melanoma. However, the role of EDARADD in TSCC remains unknown. The aim of the present investigation was to explore whether EDARADD plays a role in the biological function of TSCC. Immunohistochemistry was used to measure the expression of EDARADD in TSCC tissues and adjacent normal tissue. EDARADD was knocked down in a TSCC cell line in vitro using a specific lentivirus. The expression level of the EDARADD gene and the efficacy of gene knockdown were evaluated by reverse transcription-quantitative PCR, while EDARADD protein expression and the expression levels of Bcl-2, MYC and NF-κBp65 were determined by western blotting. Additionally, MTT assays, colony formation assays and apoptosis assays were carried out to examine the effect of EDARADD knockdown on the TSCC cells. A previous study showed that the majority of the TSCC tissues that were tested had high EDARADD expression. The expression of EDARADD both at mRNA and protein levels was significantly lower (P<0.01) after the gene was knocked down in the CAL27 cells compared with the level in control cells. Downregulation of EDARADD expression inhibited colony formation and proliferation and induced apoptosis of CAL27 cells when compared to control cells (P<0.01). Taken together, these results suggested that EDARADD may be actively involved in the progression of TSCC and that EDARADD may be a novel therapeutic target for the treatment of TSCC.

Hybrid Deep Learning Predictor for Smart Agriculture Sensing Based on Empirical Mode Decomposition and Gated Recurrent Unit Group Model.

Smart agricultural sensing has enabled great advantages in practical applications recently, making it one of the most important and valuable systems. For outdoor plantation farms, the prediction of climate data, such as temperature, wind speed, and humidity, enables the planning and control of agricultural production to improve the yield and quality of crops. However, it is not easy to accurately predict climate trends because the sensing data are complex, nonlinear, and contain multiple components. This study proposes a hybrid deep learning predictor, in which an empirical mode decomposition (EMD) method is used to decompose the climate data into fixed component groups with different frequency characteristics, then a gated recurrent unit (GRU) network is trained for each group as the sub-predictor, and finally the results from the GRU are added to obtain the prediction result. Experiments based on climate data from an agricultural Internet of Things (IoT) system verify the development of the proposed model. The prediction results show that the proposed predictor can obtain more accurate predictions of temperature, wind speed, and humidity data to meet the needs of precision agricultural production.

A Neuron-Based Kalman Filter with Nonlinear Autoregressive Model.

The control effect of various intelligent terminals is affected by the data sensing precision. The filtering method has been the typical soft computing method used to promote the sensing level. Due to the difficult recognition of the practical system and the empirical parameter estimation in the traditional Kalman filter, a neuron-based Kalman filter was proposed in the paper. Firstly, the framework of the improved Kalman filter was designed, in which the neuro units were introduced. Secondly, the functions of the neuro units were excavated with the nonlinear autoregressive model. The neuro units optimized the filtering process to reduce the effect of the unpractical system model and hypothetical parameters. Thirdly, the adaptive filtering algorithm was proposed based on the new Kalman filter. Finally, the filter was verified with the simulation signals and practical measurements. The results proved that the filter was effective in noise elimination within the soft computing solution.

Dynamic Correlation Analysis Method of Air Pollutants in Spatio-Temporal Analysis.

Pollutant analysis and pollution source tracing are critical issues in air quality management, in which correlation analysis is important for pollutant relation modeling. A dynamic correlation analysis method was proposed to meet the real-time requirement in atmospheric management. Firstly, the spatio-temporal analysis framework was designed, in which the process of data monitoring, correlation calculation, and result presentation were defined. Secondly, the core correlation calculation method was improved with an adaptive data truncation and grey relational analysis. Thirdly, based on the general framework and correlation calculation, the whole algorithm was proposed for various analysis tasks in time and space, providing the data basis for ranking and decision on pollutant effects. Finally, experiments were conducted with the practical data monitored in an industrial park of Hebei Province, China. The different pollutants in multiple monitoring stations were analyzed crosswise. The dynamic features of the results were obtained to present the variational correlation degrees from the proposed and contrast methods. The results proved that the proposed dynamic correlation analysis could quickly acquire atmospheric pollution information. Moreover, it can help to deduce the influence relation of pollutants in multiple locations.

A NeuroD1 AAV-Based Gene Therapy for Functional Brain Repair after Ischemic Injury through In Vivo Astrocyte-to-Neuron Conversion.

Adult mammalian brains have largely lost neuroregeneration capability except for a few niches. Previous studies have converted glial cells into neurons, but the total number of neurons generated is limited and the therapeutic potential is unclear. Here, we demonstrate that NeuroD1-mediated in situ astrocyte-to-neuron conversion can regenerate a large number of functional new neurons after ischemic injury. Specifically, using NeuroD1 adeno-associated virus (AAV)-based gene therapy, we were able to regenerate one third of the total lost neurons caused by ischemic injury and simultaneously protect another one third of injured neurons, leading to a significant neuronal recovery. RNA sequencing and immunostaining confirmed neuronal recovery after cell conversion at both the mRNA level and protein level. Brain slice recordings found that the astrocyte-converted neurons showed robust action potentials and synaptic responses at 2 months after NeuroD1 expression. Anterograde and retrograde tracing revealed long-range axonal projections from astrocyte-converted neurons to their target regions in a time-dependent manner. Behavioral analyses showed a significant improvement of both motor and cognitive functions after cell conversion. Together, these results demonstrate that in vivo cell conversion technology through NeuroD1-based gene therapy can regenerate a large number of functional new neurons to restore lost neuronal functions after injury.

Spatio-Temporal Prediction for the Monitoring-Blind Area of Industrial Atmosphere Based on the Fusion Network.

The monitoring-blind area exists in the industrial park because of private interest and limited administrative power. As the atmospheric quality in the blind area impacts the environment management seriously, the prediction and inference of the blind area is explored in this paper. Firstly, the fusion network framework was designed for the solution of "Circumjacent Monitoring-Blind Area Inference". In the fusion network, the nonlinear autoregressive network was set up for the time series prediction of circumjacent points, and the full connection layer was built for the nonlinear relation fitting of multiple points. Secondly, the physical structure and learning method was studied for the sub-elements in the fusion network. Thirdly, the spatio-temporal prediction algorithm was proposed based on the network for the blind area monitoring problem. Finally, the experiment was conducted with the practical monitoring data in an industrial park in Hebei Province, China. The results show that the solution is feasible for the blind area analysis in the view of spatial and temporal dimensions.

Co-disposition of chitosan nanoparticles by multi types of hepatic cells and their subsequent biological elimination: the mechanism and kinetic studies at the cellular and animal levels.

Background: The clearance of nanomaterials (NMs) from the liver is essential for clinical safety, and their hepatic clearance is primarily determined by the co-disposition process of various types of hepatic cells. Studies of this process and the subsequent clearance routes are urgently needed for organic NMs, which are used as drug carriers more commonly than the inorganic ones. Materials and methods: In this study, the co-disposition of chitosan-based nanoparticles (CsNps) by macrophages and hepatocytes at both the cellular and animal levels as well as their subsequent biological elimination were investigated. RAW264.7 and Hepa1-6 cells were used as models of Kupffer cells and hepatocytes, respectively. Results: The cellular studies showed that CsNps released from RAW264.7 cells could enter Hepa1-6 cells through both clathrin- and caveolin-mediated endocytosis. The transport from Kupffer cells to hepatocytes was also studied in mice, and it was observed that most CsNps localized to the hepatocytes after intravenous injection. Following the distribution in hepatocytes, the hepatobiliary-fecal excretion route was shown to be the primary elimination route for CsNps, besides the kidney-urinary excretion route. The elimination of CsNps in mice was a lengthy process, with a half time of about 2 months. Conclusion: The demonstration in this study of the transport of CsNps from macrophages to hepatocytes and the subsequent hepatobiliary-fecal excretion provides basic information for the future development and clinical application of NMs.