Oil-in-water Pickering emulsion stabilization with oppositely charged polysaccharide particles: chitin nanocrystals/fucoidan complexes.

BACKGROUND: Chitin nanocrystals (ChN) are insoluble particles that can be used as stabilizers for Pickering emulsions. Their unique cationic properties and antibacterial activity have generated considerable interest among researchers. However, ChN have remained largely underexplored. Furthermore, the droplets of the emulsions stabilized by ChN are as large as 10-100 µm, and their physical stability requires further improvement. Some studies have shown that the spontaneous reaction of oppositely charged particles can effectively stabilize the emulsions. Positively charged ChN and negatively charged fucoidan (F) were therefore compounded to stabilize Pickering emulsions, and the stability of these emulsions was analyzed qualitatively. RESULTS: The results showed that the composite particles comprising two polysaccharides in a mass ratio of 1:1 and at a pH of 2 (ChN1 -F1 -pH 2) possessed the lowest sulfate content (20.1%) and almost zero potential (-3 mV), indicating a high degree of neutralization of the positively charged amino group in ChN and the negatively charged sulfate group in F. Meanwhile, ChN1 -F1 -pH 2 displayed a dense network structure that improved the dispersibility and wettability (contact angle = 9.3°). Fourier transform infrared spectroscopy results confirmed that ChN and F were effectively combined through electrostatic interaction or neutralization to produce a polyelectrolyte complex. Furthermore, the particle size of the Pickering emulsion stabilized by ChN-F was significantly reduced, and the maximum size did not exceed 10 µm; the physical and storage stability also improved. The ChN1 -F1 -pH 2 emulsion presented excellent storage stability; in particular, the emulsions stabilized by ChN1 -F1 -pH 5 and ChN1 -F1 -pH 6 exhibited excellent flocculation stabilities. CONCLUSION: The size of the emulsion droplets stabilized by the oppositely charged polysaccharide particles (ChN-F complexes) reduced significantly. Furthermore, by changing the mass ratio and pH, the microstructure and binding degree of the complexes can be adjusted, thereby promoting their adsorption on the oil-water interface and improving the stability of the Pickering emulsion. © 2020 Society of Chemical Industry.

Soy/whey protein isolates: interfacial properties and effects on the stability of oil-in-water emulsions.

BACKGROUND: The adsorption of proteins at oil/water interfaces can reduce interfacial tension and increase emulsion stability. However, emulsions stabilized by soy protein isolate (SPI) are not sufficiently stable. Using SPI as a control, a theoretical basis for the adsorption behavior of mixed SPI and whey protein isolate (WPI) at the oil/water interface was established and the effects of the protein ratio and content on the emulsion stability were studied. RESULTS: Compared to SPI solution, SPI-WPI mixed solutions were found to reduce the size distribution of emulsion droplets and significantly improve the emulsion stability. Among the studied protein contents and ratios, the protein content of 0.2 g kg-1 and SPI/WPI mass ratio of 1:9 offered the lowest creaming stability index (15%), the smallest droplet size (278 nm), and the largest absolute value ζ-potential (35 mV), i.e. the emulsion stability was excellent. The largest dilatational modulus (10.08 mN m-1 ), dilatational elasticity (10.01 mN m-1 ), and dilatational viscosity (1.18 mN m-1 ), were observed with a protein content of 0.15 g kg-1 (SPI/WPI ratio of 1:9), along with a high interfacial protein adsorption capacity (47.33%). SPI-WPI complexes form a thick adsorption layer around oil droplets, resulting in an increase of the expansion modulus of the interfacial layer. CONCLUSION: SPI-WPI complexes can form a thick adsorption layer around oil droplets, resulting in increased expansion modulus of the interfacial layer, which improves emulsion stability. © 2020 Society of Chemical Industry.

Characterizing the Structural and Functional Properties of Soybean Protein Extracted from Full-Fat Soybean Flakes after Low-Temperature Dry Extrusion.

The soy protein isolates (SPI) extracted from different extruded full-fat soybean flakes (FFSF), and their conformational and functional properties were characterized. Overall, the free thiol (SH) content of SPI increased when the extrusion temperature was below 80 °C and decreased at higher temperatures. Soy glycinin (11S) showed higher stability than ß-conglycinin (7S) during extrusion. Results also indicated that the increase in some hydrophobic groups was due to the movement of hydrophobic groups from the interior to the surface of the SPI molecules at extrusion temperatures from 60 to 80 °C. However, the aggregation of SPI molecules occurred at extrusion temperatures of 90 and 100 °C, with decreasing levels of hydrophobic groups. The extrusion temperature negatively affected the emulsifying activity index (EAI); on the other side, it positively affected the emulsifying stability index (ESI), compared to unextruded SPI.

Analyzing changes in volatile flavor compounds of soy protein isolate during ultrasonic-thermal synergistic treatments using electronic nose and HS-SPME-GC-MS combined with chemometrics.

The beany flavor of soy protein isolate (SPI) creates barriers to their application in food processing. This study investigated the effect of ultrasonic-thermal synergistic treatments, combined with vacuum degassing, on the removal of volatile compounds from SPI. The results revealed that ultrasonic-thermal synergistic treatments altered protein secondary structure and increased fluorescence intensity and surface hydrophobicity, which affected the flavor-binding ability of protein, resulting in reduced electronic nose sensor response values. At synergistic treatment (350 W, 120 ℃ and 150 s), the content of hexanal, (E)-2-hexenal, and 1-octen-3-ol reduced by 70.60 %, 95.60 % and 61.23 %. (E)-2-nonenal and 2-pentylfuran were not detected. Chemometric analysis indicated significant flavor differences between control and treated SPI. Furthermore, α-helix, ß-sheet, ß-turn, and surface hydrophobicity highly correlated with volatile compounds through correlation analysis, indicating that altered protein structure affected interactions with volatile compounds. The study reduced beany flavor and further expanded the range of applications of plant protein in food industry.

Deciphering the interaction mechanism between soy protein isolate and fat-soluble anthocyanin on experiments and molecular simulations.

In this work, the acylated anthocyanin (Ca-An) was prepared by enzymatic modification of black rice anthocyanin with caffeic acid, and the binding mechanism of Ca-An to soybean protein isolate (SPI) was investigated by experiments and computer simulation to expand the potential application of anthocyanin in food industry. Multi-spectroscopic studies revealed that the stable binding of Ca-An to SPI induced the folding of protein polypeptide chain, which transformed the secondary structure of SPI trended to be flexible. The microenvironment of protein was transformed from hydrophobic to hydrophilic, while tyrosine played dominant role in quenching process. The binding sites and forces of the complexes were determined by computer simulation for further explored. The protein conformation of the 7S and 11S binding regions to Ca-An changed, and the amino acid microenvironment shifted to hydrophilic after binding. The results showed that more non-polar amino acids existed in the binding sites, while in binding process van der Waals forces and hydrogen bonding played a major role hydrophobicity played a minor role. Based on MM-PBSA analysis, the binding constants of 7S-Ca-An and 11S-Ca-An were 0.518 × 106 mol-1 and 5.437 × 10-3 mol-1, respectively. This information provides theoretical guidance for further studying the interaction between modified anthocyanins and biomacromolecules.

Self-Supervised Representation Distribution Learning for Reliable Data Augmentation in Histopathology WSI Classification.

Multiple instance learning (MIL) based whole slide image (WSI) classification is often carried out on the representations of patches extracted from WSI with a pre-trained patch encoder. The performance of classification relies on both patch-level representation learning and MIL classifier training. Most MIL methods utilize a frozen model pre-trained on ImageNet or a model trained with self-supervised learning on histopathology image dataset to extract patch image representations and then fix these representations in the training of the MIL classifiers for efficiency consideration. However, the invariance of representations cannot meet the diversity requirement for training a robust MIL classifier, which has significantly limited the performance of the WSI classification. In this paper, we propose a Self-Supervised Representation Distribution Learning framework (SSRDL) for patch-level representation learning with an online representation sampling strategy (ORS) for both patch feature extraction and WSI-level data augmentation. The proposed method was evaluated on three datasets under three MIL frameworks. The experimental results have demonstrated that the proposed method achieves the best performance in histopathology image representation learning and data augmentation and outperforms state-of-the-art methods under different WSI classification frameworks. The code is available at https://github.com/lazytkm/SSRDL.

Soybean protein isolate-sodium alginate double network emulsion gels: Mechanism of formation and improved freeze-thaw stability.

Soybean protein isolate (SPI) is widely used in the food industry. However, SPI-based emulsion gels tend to aggregate and undergo oiling-off during freeze-thawing. In this study, emulsion gels were prepared by a combination of heat treatment and ionic cross-linking using SPI and sodium alginate (SA) as raw materials. The focus was on exploring the mechanistic effects of the SPI-SA double network structure on the freeze-thaw stability of emulsion gels. The results showed that the addition of SA could form different types of network structures with SPI, due to different degrees of phase separation. In addition, SA appearing on the SPI network indicated that the addition of Ca2+ shielded the electrostatic repulsion between SPI and SA to form SPI-SA complexes. The disappearance of the characteristic peaks of SA and SPI in Fourier transform infrared spectroscopy analysis also confirmed this view. Low-field nuclear magnetic resonance data revealed that SA played a role in restricting water migration within the emulsion gels, increasing bound water content, and thereby improving the water-holding capacity of the emulsion gels. Therefore, the incorporation of SA improved the freeze-thaw stability of SPI emulsion gels. These findings offer a theoretical basis and technical support for SPI application in frozen products.

Histopathology language-image representation learning for fine-grained digital pathology cross-modal retrieval.

Large-scale digital whole slide image (WSI) datasets analysis have gained significant attention in computer-aided cancer diagnosis. Content-based histopathological image retrieval (CBHIR) is a technique that searches a large database for data samples matching input objects in both details and semantics, offering relevant diagnostic information to pathologists. However, the current methods are limited by the difficulty of gigapixels, the variable size of WSIs, and the dependence on manual annotations. In this work, we propose a novel histopathology language-image representation learning framework for fine-grained digital pathology cross-modal retrieval, which utilizes paired diagnosis reports to learn fine-grained semantics from the WSI. An anchor-based WSI encoder is built to extract hierarchical region features and a prompt-based text encoder is introduced to learn fine-grained semantics from the diagnosis reports. The proposed framework is trained with a multivariate cross-modal loss function to learn semantic information from the diagnosis report at both the instance level and region level. After training, it can perform four types of retrieval tasks based on the multi-modal database to support diagnostic requirements. We conducted experiments on an in-house dataset and a public dataset to evaluate the proposed method. Extensive experiments have demonstrated the effectiveness of the proposed method and its advantages to the present histopathology retrieval methods. The code is available at https://github.com/hudingyi/FGCR.

TFormer: A throughout fusion transformer for multi-modal skin lesion diagnosis.

Multi-modal skin lesion diagnosis (MSLD) has achieved remarkable success by modern computer-aided diagnosis (CAD) technology based on deep convolutions. However, the information aggregation across modalities in MSLD remains challenging due to severity unaligned spatial resolution (e.g., dermoscopic image and clinical image) and heterogeneous data (e.g., dermoscopic image and patients' meta-data). Limited by the intrinsic local attention, most recent MSLD pipelines using pure convolutions struggle to capture representative features in shallow layers, thus the fusion across different modalities is usually done at the end of the pipelines, even at the last layer, leading to an insufficient information aggregation. To tackle the issue, we introduce a pure transformer-based method, which we refer to as "Throughout Fusion Transformer (TFormer)", for sufficient information integration in MSLD. Different from the existing approaches with convolutions, the proposed network leverages transformer as feature extraction backbone, bringing more representative shallow features. We then carefully design a stack of dual-branch hierarchical multi-modal transformer (HMT) blocks to fuse information across different image modalities in a stage-by-stage way. With the aggregated information of image modalities, a multi-modal transformer post-fusion (MTP) block is designed to integrate features across image and non-image data. Such a strategy that information of the image modalities is firstly fused then the heterogeneous ones enables us to better divide and conquer the two major challenges while ensuring inter-modality dynamics are effectively modeled. Experiments conducted on the public Derm7pt dataset validate the superiority of the proposed method. Our TFormer achieves an average accuracy of 77.99% and diagnostic accuracy of 80.03% , which outperforms other state-of-the-art methods. Ablation experiments also suggest the effectiveness of our designs. The codes can be publicly available from https://github.com/zylbuaa/TFormer.git.

Grain Quality Characterization before and after Processing.

Grains are an important part of a healthy diet, and provide most of the daily calories and nutrients [...].

Enzymatic Acylation of Black Rice Anthocyanins and Evaluation of Antioxidant Capacity and Stability of Their Derivatives.

In this study, the structure of the anthocyanin fractions isolated from black rice (Oryza sativa L.) was modified by the enzyme catalysis method using caffeic acid as an acyl donor. At the same time, the effects of the acylation on the lipophilicity, antioxidant activity, and stability of black rice anthocyanins were comprehensively evaluated. The structural analyses of acylated derivatives based on ultraviolet-visible spectroscopy, Fourier-transform infrared spectroscopy, ultra-high-performance liquid chromatography-high-resolution mass spectrometry, and thermogravimetric analysis revealed that caffeic acid was efficiently grafted onto the anthocyanins of black rice through an acylated reaction, while the acylation binding site was on glucoside. When the mass ratios of anthocyanins to caffeic acid were 1:1, the A319/AVis-max value of acylated anthocyanins reached 6.37. Meanwhile, the lipophilicity of acylated derivatives was enhanced. The antioxidant capacity (DPPH and FRAP) and stability (thermal, pH, and light stability) were significantly increased. Overall, the study results provide deeper insights into controlling anthocyanin homeostasis in food processing, broadening the application of colored grain products.

Skin lesion classification based on two-modal images using a multi-scale fully-shared fusion network.

BACKGROUND AND OBJECTIVE: Due to the complexity of skin lesion features, computer-aided diagnosis of skin diseases based on multi-modal images is considered a challenging task. Dermoscopic images and clinical images are commonly used to diagnose skin diseases in clinical scenarios, and the complementarity of their features promotes the research of multi-modality classification in the computer-aided diagnosis field. Most current methods focus on the fusion between modalities and ignore the complementary information within each of them, which leads to the loss of the intra-modality relation. Multi-modality models for integrating features both within single modalities and across multiple modalities are limited in the literature. Therefore, a multi-modality model based on dermoscopic and clinical images is proposed to address this issue. METHODS: We propose a Multi-scale Fully-shared Fusion Network (MFF-Net) that gathers features of dermoscopic images and clinical images for skin lesion classification. In MFF-Net, the multi-scale fusion structure combines deep and shallow features within individual modalities to reduce the loss of spatial information in high-level feature maps. Then Dermo-Clinical Block (DCB) integrates the feature maps from dermoscopic images and clinical images through channel-wise concatenation and using a fully-shared fusion strategy that explores complementary information at different stages. RESULTS: We validated our model on a four-class two-modal skin diseases dataset, and proved that the proposed multi-scale structure, the fusion module DCBs, and the fully-shared fusion strategy improve the performance of MFF-Net independently. Our method achieved the highest average accuracy of 72.9% on the 7-point checklist dataset, outperforming the state-of-the-art single-modality and multi-modality methods with an accuracy boost of 7.1% and 3.4%, respectively. CONCLUSIONS: The multi-scale fusion structure demonstrates the significance of intra-modality relations between clinical images and dermoscopic images. The proposed network combined with the multi-scale structure, DCBs, and the fully-shared fusion strategy, can effectively integrate the features of the skin lesions across the two modalities and achieved a promising accuracy among different skin diseases.

Innovative minimally invasive implants for osteoporosis vertebral compression fractures.

With increasing population aging, osteoporosis vertebral compression fractures (OVCFs), resulting in severe back pain and functional impairment, have become progressively common. Percutaneous vertebroplasty (PVP) and percutaneous kyphoplasty (PKP) as minimally invasive procedures have revolutionized OVCFs treatment. However, PVP- and PKP-related complications, such as symptomatic cement leakage and adjacent vertebral fractures, continue to plague physicians. Consequently, progressively more implants for OVCFs have been developed recently to overcome the shortcomings of traditional procedures. Therefore, we conducted a literature review on several new implants for OVCFs, including StaXx FX, Vertebral Body Stenting, Vesselplasty, Sky Bone Expander, Kiva, Spine Jack, Osseofix, Optimesh, Jack, and V-strut. Additionally, this review highlights the individualized applications of these implants for OVCFs. Nevertheless, current clinical studies on these innovative implants remain limited. Future prospective, randomized, and controlled studies are needed to elucidate the effectiveness and indications of these new implants for OVCFs.

Kernel Attention Transformer for Histopathology Whole Slide Image Analysis and Assistant Cancer Diagnosis.

Transformer has been widely used in histopathology whole slide image analysis. However, the design of token-wise self-attention and positional embedding strategy in the common Transformer limits its effectiveness and efficiency when applied to gigapixel histopathology images. In this paper, we propose a novel kernel attention Transformer (KAT) for histopathology WSI analysis and assistant cancer diagnosis. The information transmission in KAT is achieved by cross-attention between the patch features and a set of kernels related to the spatial relationship of the patches on the whole slide images. Compared to the common Transformer structure, KAT can extract the hierarchical context information of the local regions of the WSI and provide diversified diagnosis information. Meanwhile, the kernel-based cross-attention paradigm significantly reduces the computational amount. The proposed method was evaluated on three large-scale datasets and was compared with 8 state-of-the-art methods. The experimental results have demonstrated the proposed KAT is effective and efficient in the task of histopathology WSI analysis and is superior to the state-of-the-art methods.

Effects of ultrasonic treatment on the structural, functional properties and beany flavor of soy protein isolate: Comparison with traditional thermal treatment.

This research explored the influences of ultrasonic and thermal treatments on the structure, functional properties, and beany flavor of soy protein isolate (SPI). In comparison with traditional thermal treatment, ultrasonic treatment effectively induced protein structural unfolding and exposure of hydrophobic groups, which reduced relative content of α-helix, increased relative content of ß-turn, ß-sheet and random coil, and improved the solubility, emulsifying and foaming properties of SPI. Both treatments significantly decreased the species and contents of flavor compounds, such as hexanal, (E)-2-nonenal, (Z)-2-heptenal and (E)-2-hexenal in SPI. The relative content of hexanal in the major beany flavor compound decreased from 11.69% to 6.13% and 5.99% at 350 W ultrasonic power and 150 s thermal treatment procedure, respectively. After ultrasonic treatment, structural changes in SPI were significantly correlated with functional properties but showed a weak correlation with flavor. Conversely, the opposite trend was observed for thermal treatment. Thus, using ultrasonic treatment to induce and stabilise the denatured state of proteins is feasible to improve the functional properties and beany flavor of SPI.

Curcumin-loaded oil body emulsions prepared by an ultrasonic and pH-driven method: Fundamental properties, stability, and digestion characteristics.

In this study, oil bodies (OBs) loaded with curcumin (Cur) were successfully prepared via an ultrasonic and pH-driven method. Ultrasonic treatment significantly improved the encapsulation efficiency (EE) and loading capacity (LC) of Cur, producing OB particles with small size, uniform distribution, and high ζ-potential absolute values. When the ultrasonic power was 200 W, the EE, LC, and ζ-potential absolute value were the greatest (88.27 %, 0.044 %, and -25.71 mV, respectively), and the OBs possessed the highest yellowness, representing the best treatment result. The confocal laser scanning microscopy (CLSM) and cryo-scanning electron microscopy (cryo-SEM) results was also intuitionally shown that. Moreover, circular dichroism (CD) proved that ultrasonic treatment could unfold the surface protein structure, further enhancing the stability. Therefore, the cream index (CI), peroxide value (POV), and thiobarbituric acid reactive substances (TBARS) were the lowest when the ultrasonic power was 200 W. In this case, the Cur loaded in OBs was well protected against hostile conditions, evidenced by the highest Cur retention rate and the lowest degradation rate constant. Finally, the in vitro gastrointestinal digestion simulation results showed that the ultrasonic treatment effectively increased the release of FFA, bioaccessibility, and stability of Cur, especially when the ultrasonic power was 200 W. This research offers a new OB-based delivery system to stabilize, deliver, and protect Cur for food processing.

A rotation meanout network with invariance for dermoscopy image classification and retrieval.

The computer-aided diagnosis (CAD) system can provide a reference basis for the clinical diagnosis of skin diseases. Convolutional neural networks (CNNs) can not only extract visual elements such as colors and shapes but also semantic features. As such they have made great improvements in many tasks of dermoscopy images. The imaging of dermoscopy has no principal orientation, indicating that there are a large number of skin lesion rotations in the datasets. However, CNNs lack rotation invariance, which is bound to affect the robustness of CNNs against rotations. To tackle this issue, we propose a rotation meanout (RM) network to extract rotation-invariant features from dermoscopy images. In RM, each set of rotated feature maps corresponds to a set of outputs of the weight-sharing convolutions and they are fused using meanout strategy to obtain the final feature maps. Through theoretical derivation, the proposed RM network is rotation-equivariant and can extract rotation-invariant features when followed by the global average pooling (GAP) operation. The extracted rotation-invariant features can better represent the original data in classification and retrieval tasks for dermoscopy images. The RM is a general operation, which does not change the network structure or increase any parameters, and can be flexibly embedded in any part of CNNs. Extensive experiments are conducted on a dermoscopy image dataset. The results show that our method outperforms other anti-rotation methods and achieves great improvements in skin disease classification and retrieval tasks, indicating the potential of rotation invariance in the field of dermoscopy images.

Dermoscopic image retrieval based on rotation-invariance deep hashing.

Dermoscopic image retrieval technology can provide dermatologists with valuable information such as similar confirmed skin disease cases and diagnosis reports to assist doctors in their diagnosis. In this study, we design a dermoscopic image retrieval algorithm using convolutional neural networks (CNNs) and hash coding. A hybrid dilated convolution spatial attention module is proposed, which can focus on important information and suppress irrelevant information based on the complex morphological characteristics of dermoscopic images. Furthermore, we also propose a Cauchy rotation invariance loss function in view of the skin lesion target without the main direction. This function constrains CNNs to learn output differences in samples from different angles and to make CNNs obtain a certain rotation invariance. Extensive experiments are conducted on dermoscopic image datasets to verify the effectiveness and versatility of the proposed module, algorithm, and loss function. Experiment results show that the rotation-invariance deep hashing network with the proposed spatial attention module obtains better performance on the task of dermoscopic image retrieval.

Encoding histopathology whole slide images with location-aware graphs for diagnostically relevant regions retrieval.

Content-based histopathological image retrieval (CBHIR) has become popular in recent years in histopathological image analysis. CBHIR systems provide auxiliary diagnosis information for pathologists by searching for and returning regions that are contently similar to the region of interest (ROI) from a pre-established database. It is challenging and yet significant in clinical applications to retrieve diagnostically relevant regions from a database consisting of histopathological whole slide images (WSIs). In this paper, we propose a novel framework for regions retrieval from WSI database based on location-aware graphs and deep hash techniques. Compared to the present CBHIR framework, both structural information and global location information of ROIs in the WSI are preserved by graph convolution and self-attention operations, which makes the retrieval framework more sensitive to regions that are similar in tissue distribution. Moreover, benefited from the graph structure, the proposed framework has good scalability for both the size and shape variation of ROIs. It allows the pathologist to define query regions using free curves according to the appearance of tissue. Thirdly, the retrieval is achieved based on the hash technique, which ensures the framework is efficient and adequate for practical large-scale WSI database. The proposed method was evaluated on an in-house endometrium dataset with 2650 WSIs and the public ACDC-LungHP dataset. The experimental results have demonstrated that the proposed method achieved a mean average precision above 0.667 on the endometrium dataset and above 0.869 on the ACDC-LungHP dataset in the task of irregular region retrieval, which are superior to the state-of-the-art methods. The average retrieval time from a database containing 1855 WSIs is 0.752 ms. The source code is available at https://github.com/zhengyushan/lagenet.

Effect of nanoemulsion particle size on the bioavailability and bioactivity of perilla oil in rats.

The aim of this study was to investigate the bioavailability and bioactivity of perilla (Perilla frutescens) oil nanoemulsions prepared at different homogenization pressures by measuring the weight, fatty acid profile, and antioxidant and anti-inflammatory properties in rats. The high-pressure homogenization significantly reduced the particle size of perilla oil nanoemulsions and enhanced their stability, and the minimum particle size was 293.87 ± 6.55 nm at 120 MPa. There was an increase in the weight and fatty acid levels in the plasma and liver of test group rats. The highest glutathione (GSH) and the lowest malondialdehyde (MDA) levels of 18.76 ± 10.51 mg GSH/g prot and 20.27 ± 2.46 nmol/mg prot, respectively, were recorded in rats administrated perilla oil nanoemulsions prepared at 120 MPa. However, there was no significant difference in superoxide dismutase activity (SOD) between the groups. The interferon-gamma (IL-γ), interleukin-1 beta (IL-1ß), IL-6 (interleukin-6), and IL-8 (interleukin-8) levels in the test groups were lower than those in the blank and control groups at 8 hr after lipopolysaccharide injection. The IL-1ß, IL-6, and IL-8 levels were 49.52 ± 14.06, 90.13 ± 6.04, and 419.71 ± 32.03 ng/L, respectively, in rats treated with perilla oil nanoemulsions prepared at 120 MPa. Both perilla oil and its nanoemulsions decreased estradiol levels and damaged the ovaries. Overall, our findings show that the test nanoemulsions enhanced the bioavailability of perilla oil, which resulted in enhanced antioxidant and anti-inflammatory responses; thus, we provide a new approach to deliver perilla oil. PRACTICAL APPLICATION: Nanoemulsions can be used to deliver drugs and bioactive compounds, and perilla oil nanoemulsions can be used in healthcare products and beverage industries.