Source-free unsupervised domain adaptation: A survey.

Unsupervised domain adaptation (UDA) via deep learning has attracted appealing attention for tackling domain-shift problems caused by distribution discrepancy across different domains. Existing UDA approaches highly depend on the accessibility of source domain data, which is usually limited in practical scenarios due to privacy protection, data storage and transmission cost, and computation burden. To tackle this issue, many source-free unsupervised domain adaptation (SFUDA) methods have been proposed recently, which perform knowledge transfer from a pre-trained source model to the unlabeled target domain with source data inaccessible. A comprehensive review of these works on SFUDA is of great significance. In this paper, we provide a timely and systematic literature review of existing SFUDA approaches from a technical perspective. Specifically, we categorize current SFUDA studies into two groups, i.e., white-box SFUDA and black-box SFUDA, and further divide them into finer subcategories based on different learning strategies they use. We also investigate the challenges of methods in each subcategory, discuss the advantages/disadvantages of white-box and black-box SFUDA methods, conclude the commonly used benchmark datasets, and summarize the popular techniques for improved generalizability of models learned without using source data. We finally discuss several promising future directions in this field.

Investigation of cellular communication and signaling pathways in tumor microenvironment for high TP53-expressing osteosarcoma cells through single-cell RNA sequencing.

Osteosarcoma (OS) stands as the most prevalent primary bone cancer in children and adolescents, and its limited treatment options often result in unsatisfactory outcomes, particularly for metastatic cases. The tumor microenvironment (TME) has been recognized as a crucial determinant in OS progression. However, the intercellular dynamics between high TP53-expressing OS cells and neighboring cell types within the TME are yet to be thoroughly understood. In our study, we harnessed the single-cell RNA sequencing (scRNA-seq) technology in combination with the computational tool-Cellchat, aiming to elucidate the intercellular communication networks present within OS. Through meticulous quantitative inference and subsequent analysis of these networks, we succeeded in identifying significant signaling pathways connecting high TP53-expressing OS cells with proximate cell types, namely Macrophages, Monocytes, Endothelial Cells, and PVLs. This research brings forth a nuanced understanding of the intricate patterns and coordination involved in the TME's intercellular communication signals. These findings not only provide profound insights into the molecular mechanisms underpinning OS but also indicate potential therapeutic targets that could revolutionize treatment strategies.

Dissecting lysosomal viscosity fluctuations in live cells and liver tissues with an ingenious NIR fluorescent probe.

Viscosity is a pivotal component in the cell microenvironment, while lysosomal viscosity fluctuation is associated with various human diseases, such as tumors and liver diseases. Herein, a near-infrared fluorescent probe (BIMM) based on merocyanine dyes was designed and synthesized for detecting lysosomal viscosity in live cells and liver tissue. The increase in viscosity restricts the free rotation of single bonds, leading to enhanced fluorescence intensity. BIMM exhibits high sensitivity and good selectivity, and is applicable to a wide pH range. BIMM has near-infrared emission, and the fluorescent intensity shows an excellent linear relationship with viscosity. Furthermore, BIMM possessing excellent lysosomes-targeting ability, and can monitor viscosity changes in live cells stimulated by dexamethasone, lipopolysaccharide (LPS), and nigericin, and differentiate between cancer cells and normal cells. Noticeably, BIMM can accurately analyze viscosity changes in various liver disease models with HepG2 cells, and is successfully utilized to visualize variations in viscosity on APAP-induced liver injury. All the results demonstrated that BIMM is a powerful wash-free tool to monitor the viscosity fluctuations in living systems.

Leveraging Brain Modularity Prior for Interpretable Representation Learning of fMRI.

Resting-state functional magnetic resonance imaging (rs-fMRI) can reflect spontaneous neural activities in the brain and is widely used for brain disorder analysis. Previous studies focus on extracting fMRI representations using machine/deep learning methods, but these features typically lack biological interpretability. The human brain exhibits a remarkable modular structure in spontaneous brain functional networks, with each module comprised of functionally interconnected brain regions-of-interest (ROIs). However, existing learning-based methods cannot adequately utilize such brain modularity prior. In this paper, we propose a brain modularity-constrained dynamic representation learning framework for interpretable fMRI analysis, consisting of dynamic graph construction, dynamic graph learning via a novel modularity-constrained graph neural network (MGNN), and prediction and biomarker detection. The designed MGNN is constrained by three core neurocognitive modules (i.e., salience network, central executive network, and default mode network), encouraging ROIs within the same module to share similar representations. To further enhance discriminative ability of learned features, we encourage the MGNN to preserve network topology of input graphs via a graph topology reconstruction constraint. Experimental results on 534 subjects with rs-fMRI scans from two datasets validate the effectiveness of the proposed method. The identified discriminative brain ROIs and functional connectivities can be regarded as potential fMRI biomarkers to aid in clinical diagnosis.

Rhodanine Derivatives Containing 5-Aryloxypyrazole Moiety as Anti-inflammatory and Anticancer Agents.

In this study, a series of rhodanine derivatives containing 5-aryloxypyrazole moiety were identified as potential agents with anti-inflammatory and anticancer properties. Most of the synthesized compounds demonstrated anti-inflammatory and anticancer activity. Notably, compound 7 g (94.1 %) exhibited significant anti-inflammatory activity compared with the reference drugs celecoxib (52.5 %) and hydrocortisone (79.4 %). Compound 7 g, at various concentrations, effectively inhibited nitric oxide (NO) production in a dose-dependent manner. Western blot results showed that compound 7 g could prevents LPS-induced expression of inflammatory mediators in macrophages. Enzyme-linked immunosorbent assay (ELISA) assay suggested that 7 g is a promising compound capable of blocking the downstream signaling of COX-2. In summary, these findings indicate that compound 7 g could be a promising candidate for further investigation.

The Essence in Selectivity of Copper-Mediated Intermolecular Nucleophilic Substitution of a meta C-H Bond in 2-Methyl-N-methoxyaniline: A Theoretical Study.

The detailed mechanism for NHC-Cu(I)-catalyzed intermolecular nucleophilic substitution of the C-H bonds at aniline (2-methyl-N-methoxyaniline) was studied via DFT methods to reveal the essence of the selectivity. Calculations revealed that the meta C-H functionalization proceeds via two nucleophilic attacks on the aromatic ring rather than a one-step meta C-H substitution to give the experimentally observed major product. The reaction is initiated by activation of the substrate via oxidative addition with an NHC-Cu(I) catalyst, through which an umpolung occurs at the ring. From the activated intermediate, methoxyl group transfer to benzyl forms a resting state, while a nucleophile can attack the ortho position of benzyl to form a more stable intermediate. The nucleophile group can then transfer to the meta position by a 1,2-Wagner-Meerwein rearrangement to form the final product through a proton shuttle. In contrast, other transfer processes affording ortho- or para-substituted products encounter higher activation barriers. This work investigates the relationship of product selectivity with the umpolung of the aromatic ring, as well as the priority of a nucleophilic attack at the ortho position of the aromatic, 1,2-Wagner-Meerwein rearrangement from the ortho-substituted intermediate, and proton shuttle from the meta-substituted intermediate.

Addressing multi-site functional MRI heterogeneity through dual-expert collaborative learning for brain disease identification.

Several studies employ multi-site rs-fMRI data for major depressive disorder (MDD) identification, with a specific site as the to-be-analyzed target domain and other site(s) as the source domain. But they usually suffer from significant inter-site heterogeneity caused by the use of different scanners and/or scanning protocols and fail to build generalizable models that can well adapt to multiple target domains. In this article, we propose a dual-expert fMRI harmonization (DFH) framework for automated MDD diagnosis. Our DFH is designed to simultaneously exploit data from a single labeled source domain/site and two unlabeled target domains for mitigating data distribution differences across domains. Specifically, the DFH consists of a domain-generic student model and two domain-specific teacher/expert models that are jointly trained to perform knowledge distillation through a deep collaborative learning module. A student model with strong generalizability is finally derived, which can be well adapted to unseen target domains and analysis of other brain diseases. To the best of our knowledge, this is among the first attempts to investigate multi-target fMRI harmonization for MDD diagnosis. Comprehensive experiments on 836 subjects with rs-fMRI data from 3 different sites show the superiority of our method. The discriminative brain functional connectivities identified by our method could be regarded as potential biomarkers for fMRI-related MDD diagnosis.

Detecting type 2 diabetes mellitus cognitive impairment using whole-brain functional connectivity.

Type 2 diabetes mellitus (T2DM) is closely linked to cognitive decline and alterations in brain structure and function. Resting-state functional magnetic resonance imaging (rs-fMRI) is used to diagnose neurodegenerative diseases, such as cognitive impairment (CI), Alzheimer's disease (AD), and vascular dementia (VaD). However, whether the functional connectivity (FC) of patients with T2DM and mild cognitive impairment (T2DM-MCI) is conducive to early diagnosis remains unclear. To answer this question, we analyzed the rs-fMRI data of 37 patients with T2DM and mild cognitive impairment (T2DM-MCI), 93 patients with T2DM but no cognitive impairment (T2DM-NCI), and 69 normal controls (NC). We achieved an accuracy of 87.91% in T2DM-MCI versus T2DM-NCI classification and 80% in T2DM-NCI versus NC classification using the XGBoost model. The thalamus, angular, caudate nucleus, and paracentral lobule contributed most to the classification outcome. Our findings provide valuable knowledge to classify and predict T2DM-related CI, can help with early clinical diagnosis of T2DM-MCI, and provide a basis for future studies.

DNA methylation entropy is associated with DNA sequence features and developmental epigenetic divergence.

Epigenetic information defines tissue identity and is largely inherited in development through DNA methylation. While studied mostly for mean differences, methylation also encodes stochastic change, defined as entropy in information theory. Analyzing allele-specific methylation in 49 human tissue sample datasets, we find that methylation entropy is associated with specific DNA binding motifs, regulatory DNA, and CpG density. Then applying information theory to 42 mouse embryo methylation datasets, we find that the contribution of methylation entropy to time- and tissue-specific patterns of development is comparable to the contribution of methylation mean, and methylation entropy is associated with sequence and chromatin features conserved with human. Moreover, methylation entropy is directly related to gene expression variability in development, suggesting a role for epigenetic entropy in developmental plasticity.

Unsupervised cross-domain functional MRI adaptation for automated major depressive disorder identification.

Resting-state functional magnetic resonance imaging (rs-fMRI) data have been widely used for automated diagnosis of brain disorders such as major depressive disorder (MDD) to assist in timely intervention. Multi-site fMRI data have been increasingly employed to augment sample size and improve statistical power for investigating MDD. However, previous studies usually suffer from significant inter-site heterogeneity caused for instance by differences in scanners and/or scanning protocols. To address this issue, we develop a novel discrepancy-based unsupervised cross-domain fMRI adaptation framework (called UFA-Net) for automated MDD identification. The proposed UFA-Net is designed to model spatio-temporal fMRI patterns of labeled source and unlabeled target samples via an attention-guided graph convolution module, and also leverage a maximum mean discrepancy constrained module for unsupervised cross-site feature alignment between two domains. To the best of our knowledge, this is one of the first attempts to explore unsupervised rs-fMRI adaptation for cross-site MDD identification. Extensive evaluation on 681 subjects from two imaging sites shows that the proposed method outperforms several state-of-the-art methods. Our method helps localize disease-associated functional connectivity abnormalities and is therefore well interpretable and can facilitate fMRI-based analysis of MDD in clinical practice.

Modularity-Constrained Dynamic Representation Learning for Interpretable Brain Disorder Analysis with Functional MRI.

Resting-state functional MRI (rs-fMRI) is increasingly used to detect altered functional connectivity patterns caused by brain disorders, thereby facilitating objective quantification of brain pathology. Existing studies typically extract fMRI features using various machine/deep learning methods, but the generated imaging biomarkers are often challenging to interpret. Besides, the brain operates as a modular system with many cognitive/topological modules, where each module contains subsets of densely inter-connected regions-of-interest (ROIs) that are sparsely connected to ROIs in other modules. However, current methods cannot effectively characterize brain modularity. This paper proposes a modularity-constrained dynamic representation learning (MDRL) framework for interpretable brain disorder analysis with rs-fMRI. The MDRL consists of 3 parts: (1) dynamic graph construction, (2) modularity-constrained spatiotemporal graph neural network (MSGNN) for dynamic feature learning, and (3) prediction and biomarker detection. In particular, the MSGNN is designed to learn spatiotemporal dynamic representations of fMRI, constrained by 3 functional modules (i.e., central executive network, salience network, and default mode network). To enhance discriminative ability of learned features, we encourage the MSGNN to reconstruct network topology of input graphs. Experimental results on two public and one private datasets with a total of 1,155 subjects validate that our MDRL outperforms several state-of-the-art methods in fMRI-based brain disorder analysis. The detected fMRI biomarkers have good explainability and can be potentially used to improve clinical diagnosis.

Improving intestinal absorption and antibacterial effect of florfenicol via nanocrystallisation technology.

To study the effects of nanocrystallisation technology on the intestinal absorption properties and antibacterial activity of florfenicol (FF). The florfenicol nanocrystals (FF-NC) were prepared by wet grinding and spray drying. Additionally, changes in particle size, charge, morphology, and dissolution of FF-NC in the long-term stability were monitored by laser particle sizer, TEM, SEM, paddle method, and the structure of FF-NC powder was characterised by nuclear magnetic resonance (NMR) test. The antibacterial activity, intestinal absorption and intestinal histocompatibility of FF-NC were investigated by the stiletto, mini broth dilution susceptibility test, in situ single-pass intestinal perfusion (SPIP) and haematoxylin-eosin (H-E) staining. After 12 months of storage, the particle size and zeta potential of FF-NC were 280.43 ± 8.21 nm and -19.64 ± 3.45 mV, and the electron microscopy results showed that FF-NC were nearly circular with no adhesion between particles. In addition, the drug loading, encapsulation efficiency, and dissolution of FF-NC did not change significantly during storage. The inhibition zone of FF-NC against Escherichia coli and Staphylococcus aureus was 21.37 ± 1.70 mm and 25.17 ± 2.47 mm, respectively. Compared with the FF, the minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of FF-NC are reduced, and the absorption rate constant (Ka) and efficient permeability coefficient (Peff) of FF-NC in the three intestinal segments were increased by 1.28, 0.25, and 9.10 times and 0.59, 0.17, and 6.0 times, respectively. The results of tissue sections showed that FF-NC had little damage to the small intestinal. Nanocrystallisation technology is an effective method to increase the intestinal absorption and antibacterial activity of FF.

Domain-Prior-Induced Structural MRI Adaptation for Clinical Progression Prediction of Subjective Cognitive Decline.

Growing evidence shows that subjective cognitive decline (SCD) among elderly individuals is the possible pre-clinical stage of Alzheimer's disease (AD). To prevent the potential disease conversion, it is critical to investigate biomarkers for SCD progression. Previous learning-based methods employ T1-weighted magnetic resonance imaging (MRI) data to aid the future progression prediction of SCD, but often fail to build reliable models due to the insufficient number of subjects and imbalanced sample classes. A few studies suggest building a model on a large-scale AD-related dataset and then applying it to another dataset for SCD progression via transfer learning. Unfortunately, they usually ignore significant data distribution gaps between different centers/domains. With the prior knowledge that SCD is at increased risk of underlying AD pathology, we propose a domain-prior-induced structural MRI adaptation (DSMA) method for SCD progression prediction by mitigating the distribution gap between SCD and AD groups. The proposed DSMA method consists of two parallel feature encoders for MRI feature learning in the labeled source domain and unlabeled target domain, an attention block to locate potential disease-associated brain regions, and a feature adaptation module based on maximum mean discrepancy (MMD) for cross-domain feature alignment. Experimental results on the public ADNI dataset and an SCD dataset demonstrate the superiority of our method over several state-of-the-arts.

Deep Learning Model for Predicting the Pathological Complete Response to Neoadjuvant Chemoradiotherapy of Locally Advanced Rectal Cancer.

Objective: This study aimed to develop an artificial intelligence model for predicting the pathological complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) of locally advanced rectal cancer (LARC) using digital pathological images. Background: nCRT followed by total mesorectal excision (TME) is a standard treatment strategy for patients with LARC. Predicting the PCR to nCRT of LARC remine difficulty. Methods: 842 LARC patients treated with standard nCRT from three medical centers were retrospectively recruited and subgrouped into the training, testing and external validation sets. Treatment response was classified as pCR and non-pCR based on the pathological diagnosis after surgery as the ground truth. The hematoxylin & eosin (H&E)-stained biopsy slides were manually annotated and used to develop a deep pathological complete response (DeepPCR) prediction model by deep learning. Results: The proposed DeepPCR model achieved an AUC-ROC of 0.710 (95% CI: 0.595, 0.808) in the testing cohort. Similarly, in the external validation cohort, the DeepPCR model achieved an AUC-ROC of 0.723 (95% CI: 0.591, 0.844). The sensitivity and specificity of the DeepPCR model were 72.6% and 46.9% in the testing set and 72.5% and 62.7% in the external validation cohort, respectively. Multivariate logistic regression analysis showed that the DeepPCR model was an independent predictive factor of nCRT (P=0.008 and P=0.004 for the testing set and external validation set, respectively). Conclusions: The DeepPCR model showed high accuracy in predicting pCR and served as an independent predictive factor for pCR. The model can be used to assist in clinical treatment decision making before surgery.

DeepNoise: Signal and Noise Disentanglement Based on Classifying Fluorescent Microscopy Images via Deep Learning.

The high-content image-based assay is commonly leveraged for identifying the phenotypic impact of genetic perturbations in biology field. However, a persistent issue remains unsolved during experiments: the interferential technical noises caused by systematic errors (e.g., temperature, reagent concentration, and well location) are always mixed up with the real biological signals, leading to misinterpretation of any conclusion drawn. Here, we reported a mean teacher-based deep learning model (DeepNoise) that can disentangle biological signals from the experimental noises. Specifically, we aimed to classify the phenotypic impact of 1108 different genetic perturbations screened from 125,510 fluorescent microscopy images, which were totally unrecognizable by the human eye. We validated our model by participating in the Recursion Cellular Image Classification Challenge, and DeepNoise achieved an extremely high classification score (accuracy: 99.596%), ranking the 2nd place among 866 participating groups. This promising result indicates the successful separation of biological and technical factors, which might help decrease the cost of treatment development and expedite the drug discovery process. The source code of DeepNoise is available at https://github.com/Scu-sen/Recursion-Cellular-Image-Classification-Challenge.

The effect of physical activity on anhedonia in individuals with depressive symptoms.

The therapeutic effect of antidepressants has been demonstrated for anhedonia in patients with depression. However, antidepressants may cause side-effects, such as cardiovascular dysfunction. Although physical activity has minor side-effects, it may serve as an alternative for improving anhedonia and depression. We sought to investigate whether physical activity reduces the level of anhedonia in individuals with depression. Fifty-six university students with moderate depressive symptoms (Beck Depression Inventory total score > 16) were divided into three training groups: the Running Group (RG, n = 19), the Stretching Group (SG, n = 19), and the Control Group (n = 18). We employed the Monetary Incentive Delay (MID) task and the Temporal Experience of Pleasure Scale (TEPS) to evaluate hedonic capacity. All participants in the RG and SG received 8 weeks of jogging and stretching training, respectively. The RG experienced an increase in the level of arousal during anticipation of a future reward and recalled less negativity towards the loss condition. The SG exhibited enhanced scores on the Anticipatory and Consummatory Pleasure subscales of the TEPS after training. Moreover, in the RG, greater improvements in anticipatory arousal ratings for pleasure and remembered valence ratings for negative affect were associated with longer training duration, lower maximum heart rate, and higher consumed calories during training. To conclude, physical activity is effective in improving anticipatory anhedonia in individuals with depressive symptoms.

A hybrid network for automatic hepatocellular carcinoma segmentation in H&E-stained whole slide images.

Hepatocellular carcinoma (HCC), as the most common type of primary malignant liver cancer, has become a leading cause of cancer deaths in recent years. Accurate segmentation of HCC lesions is critical for tumor load assessment, surgery planning, and postoperative examination. As the appearance of HCC lesions varies greatly across patients, traditional manual segmentation is a very tedious and time-consuming process, the accuracy of which is also difficult to ensure. Therefore, a fully automated and reliable HCC segmentation system is in high demand. In this work, we present a novel hybrid neural network based on multi-task learning and ensemble learning techniques for accurate HCC segmentation of hematoxylin and eosin (H&E)-stained whole slide images (WSIs). First, three task-specific branches are integrated to enlarge the feature space, based on which the network is able to learn more general features and thus reduce the risk of overfitting. Second, an ensemble learning scheme is leveraged to perform feature aggregation, in which selective kernel modules (SKMs) and spatial and channel-wise squeeze-and-excitation modules (scSEMs) are adopted for capturing the features from different spaces and scales. Our proposed method achieves state-of-the-art performance on three publicly available datasets, with segmentation accuracies of 0.797, 0.923, and 0.765 in the PAIP, CRAG, and UHCMC&CWRU datasets, respectively, which demonstrates its effectiveness in addressing the HCC segmentation problem. To the best of our knowledge, this is also the first work on the pixel-wise HCC segmentation of H&E-stained WSIs.

Mechanistic Study of Domino Rearrangement-Promoted Meta C-H Activation in 2-Methyl-N-methoxyaniline via Cu(NHC)+: Motivation and Selectivity.

Here we report a detailed theoretical study of the mechanism of Cu+-catalyzed domino rearrangement of 2-methyl-N-methoxyaniline with a deep understanding of the unique motivation and selectivity of these migrations. We find that the Cu+ catalyst accelerates the [1,3]-methoxy migration to the methyl-bound ortho position of umpolung phenyl. The following domino transfer prefers methyl [1,2]-migration, and the rate-determining step for the whole reaction is the transfer of a proton from the phenyl ring to amine to finish the catalytic cycle.

Enhancing Betulinic Acid Dissolution Rate and Improving Antitumor Activity via Nanosuspension Constructed by Anti-Solvent Technique.

PURPOSE: The aim of this study was to prepare and evaluate betulinic acid nanosuspension (BA-NS) for new drug delivery to enhance its solubility and in vitro anti-tumor activity. METHODS: BA-NS was formulated by an anti-solvent precipitation method using the Box-Behnken design (BBD). Particle size (PS) and Zeta potential were measured by laser particle size analysis. The drug solid state after freeze drying was characterized by scanning electron microscope (SEM), transmission electron microscope (TEM), differential scanning calorimetry (DSC), X-ray powder diffraction (XRPD) and Fourier transform infrared spectroscopy (FTIR) after freeze drying. The saturation solubility and dissolution rate were determined by solubility assay and in vitro dissolution studies, respectively. The in vitro cytotoxicity assay was performed using 3-(4,5-dimethylthiazole)-2,5-diphenltetraazolium bromide (MTT) method. RESULTS: The PS was 129.7±12.2 nm having a Zeta potential of -28.1±4.5 mV and the polydispersity index (PDI) was 0.231±0.013, which confirmed that the nanosuspension was in the stable amorphous state. A series of characterization experiments demonstrated that nanoparticles retained original effective structure and existed as spherical or near-spherical nanoparticles in the nanosuspension, but the drug transferred from the crystal state to the amorphous state. The form of lyophilized BA-NS was very successful in enhancing the dissolution rate in PH-dependent way. The cytotoxicity assay revealed that BA-NS could significantly enhance the in vitro anti-proliferation against tumor cells compared to the BA suspension (BA-S). CONCLUSION: The BA-NS can remarkably improve solubility and in vitro antitumor activity, which seems very promising for the treatment of cancers in practical application.

Increased bioaffinity and anti-inflammatory activity of florfenicol nanocrystals by wet grinding method.

Context: The main objective of the current study is to improve the water solubility of florfenicol (FF) and evaluate changes in its pharmacokinetics and anti-inflammatory activity.Materials and methods: Florfenicol nanocrystals (FF-NC) were prepared by wet grinding combined with spray drying. The characterisations, pharmacokinetics, and anti-inflammatory activity of FF-NC were evaluated.Results: The particle size, polydispersity index (PDI), and zeta potential of FF-NC were 276.4 ± 19.4 nm, 0.166 ± 0.011, and -18.66 ± 5.25 mV, respectively. Compared with FF, FF-NC showed a better dissolution rate in media at different pH. Pharmacokinetic experiments showed the area under the curve (AUC0-t), maximum concentration (Cmax), and mean residence time (MRT) of FF-NC were about 4.62-fold, 2.86-fold, and 1.68-fold higher compared with FF, respectively. In vitro anti-inflammatory experiments showed that FF inhibited the secretion of tumour necrosis factor-α (TNF-α), interleukin-6 (IL-6), and synthesis of NO in a dose-dependent manner, while FF-NC showed a stronger anti-inflammatory effect than FF under the same dose.Conclusion: FF-NC are an effective way to improve the bioaffinity and anti-inflammatory effects of FF.