GNUV201, a novel human/mouse cross-reactive and low pH-selective anti-PD-1 monoclonal antibody for cancer immunotherapy.

BACKGROUND: Several PD-1 antibodies approved as anti-cancer therapies work by blocking the interaction of PD-1 with its ligand PD-L1, thus restoring anti-cancer T cell activities. These PD-1 antibodies lack inter-species cross-reactivity, necessitating surrogate antibodies for preclinical studies, which may limit the predictability and translatability of the studies. RESULTS: To overcome this limitation, we have developed an inter-species cross-reactive PD-1 antibody, GNUV201, by utilizing an enhanced diversity mouse platform (SHINE MOUSE™). GNUV201 equally binds to human PD-1 and mouse PD-1, equally inhibits the binding of human PD-1/PD-L1 and mouse PD-1/PD-L1, and effectively suppresses tumor growth in syngeneic mouse models. The epitope of GNUV201 mapped to the "FG loop" of hPD-1, distinct from those of Keytruda® ("C'D loop") and Opdivo® (N-term). Notably, the structural feature where the protruding epitope loop fits into GNUV201's binding pocket supports the enhanced binding affinity due to slower dissociation (8.7 times slower than Keytruda®). Furthermore, GNUV201 shows a stronger binding affinity at pH 6.0 (5.6 times strong than at pH 7.4), which mimics the hypoxic and acidic tumor microenvironment (TME). This phenomenon is not observed with marketed antibodies (Keytruda®, Opdivo®), implying that GNUV201 achieves more selective binding to and better occupancy on PD-1 in the TME. CONCLUSIONS: In summary, GNUV201 exhibited enhanced affinity for PD-1 with slow dissociation and preferential binding in TME-mimicking low pH. Human/monkey/mouse inter-species cross-reactivity of GNUV201 could enable more predictable and translatable efficacy and toxicity preclinical studies. These results suggest that GNUV201 could be an ideal antibody candidate for anti-cancer drug development.

GammaGAN: Gamma-Scaled Class Embeddings for Conditional Video Generation.

In this paper, we propose a new model for conditional video generation (GammaGAN). Generally, it is challenging to generate a plausible video from a single image with a class label as a condition. Traditional methods based on conditional generative adversarial networks (cGANs) often encounter difficulties in effectively utilizing a class label, typically by concatenating a class label to the input or hidden layer. In contrast, the proposed GammaGAN adopts the projection method to effectively utilize a class label and proposes scaling class embeddings and normalizing outputs. Concretely, our proposed architecture consists of two streams: a class embedding stream and a data stream. In the class embedding stream, class embeddings are scaled to effectively emphasize class-specific differences. Meanwhile, the outputs in the data stream are normalized. Our normalization technique balances the outputs of both streams, ensuring a balance between the importance of feature vectors and class embeddings during training. This results in enhanced video quality. We evaluated the proposed method using the MUG facial expression dataset, which consists of six facial expressions. Compared with the prior conditional video generation model, ImaGINator, our model yielded relative improvements of 1.61%, 1.66%, and 0.36% in terms of PSNR, SSIM, and LPIPS, respectively. These results suggest potential for further advancements in conditional video generation.

Image Recommendation System Based on Environmental and Human Face Information.

With the advancement of computer hardware and communication technologies, deep learning technology has made significant progress, enabling the development of systems that can accurately estimate human emotions. Factors such as facial expressions, gender, age, and the environment influence human emotions, making it crucial to understand and capture these intricate factors. Our system aims to recommend personalized images by accurately estimating human emotions, age, and gender in real time. The primary objective of our system is to enhance user experiences by recommending images that align with their current emotional state and characteristics. To achieve this, our system collects environmental information, including weather conditions and user-specific environment data through APIs and smartphone sensors. Additionally, we employ deep learning algorithms for real-time classification of eight types of facial expressions, age, and gender. By combining this facial information with the environmental data, we categorize the user's current situation into positive, neutral, and negative stages. Based on this categorization, our system recommends natural landscape images that are colorized using Generative Adversarial Networks (GANs). These recommendations are personalized to match the user's current emotional state and preferences, providing a more engaging and tailored experience. Through rigorous testing and user evaluations, we assessed the effectiveness and user-friendliness of our system. Users expressed satisfaction with the system's ability to generate appropriate images based on the surrounding environment, emotional state, and demographic factors such as age and gender. The visual output of our system significantly impacted users' emotional responses, resulting in a positive mood change for most users. Moreover, the system's scalability was positively received, with users acknowledging its potential benefits when installed outdoors and expressing a willingness to continue using it. Compared to other recommender systems, our integration of age, gender, and weather information provides personalized recommendations, contextual relevance, increased engagement, and a deeper understanding of user preferences, thereby enhancing the overall user experience. The system's ability to comprehend and capture intricate factors that influence human emotions holds promise in various domains, including human-computer interaction, psychology, and social sciences.

The High-Resolution Structure Reveals Remarkable Similarity in PD-1 Binding of Cemiplimab and Dostarlimab, the FDA-Approved Antibodies for Cancer Immunotherapy.

Multiple tumors have responded well to immunotherapies, which use monoclonal antibodies to block the immune checkpoint proteins and reactivate the T-cell immune response to cancer cells. Significantly, the anti-PD-1 antibodies pembrolizumab and nivolumab, which were approved in 2014, have revolutionized cancer therapy, demonstrating dramatic improvement and longer duration. The US FDA authorized the third anti-PD-1 medication, cemiplimab, in 2018 for use in patients with cutaneous squamous cell carcinoma. To further understand the molecular mechanism of the antibody drug, we now reveal the intricate structure of PD-1 in complex with the cemiplimab Fab at a resolution of 1.98 Å. The cemiplimab-PD-1 interaction preoccupies the space for PD-L1 binding with a greater binding affinity than the PD-1/PD-L1 interaction, which is the basis for the PD-1 blocking mechanism. The structure reveals that cemiplimab and dostarlimab are significantly similar in PD-1 binding, although the precise interactions differ. A comparative investigation of PD-1 interactions with the four FDA-approved antibodies reveals that the BC, C'D, and FG loops of PD-1 adopt distinct conformations for optimal interaction with the antibodies. The structural characteristics in this work could be helpful information for developing more potent anti-PD-1 biologics against cancer.

Efficient Multi-Scale Stereo-Matching Network Using Adaptive Cost Volume Filtering.

While recent deep learning-based stereo-matching networks have shown outstanding advances, there are still some unsolved challenges. First, most state-of-the-art stereo models employ 3D convolutions for 4D cost volume aggregation, which limit the deployment of networks for resource-limited mobile environments owing to heavy consumption of computation and memory. Although there are some efficient networks, most of them still require a heavy computational cost to incorporate them to mobile computing devices in real-time. Second, most stereo networks indirectly supervise cost volumes through disparity regression loss by using the softargmax function. This causes problems in ambiguous regions, such as the boundaries of objects, because there are many possibilities for unreasonable cost distributions which result in overfitting problem. A few works deal with this problem by generating artificial cost distribution using only the ground truth disparity value that is insufficient to fully regularize the cost volume. To address these problems, we first propose an efficient multi-scale sequential feature fusion network (MSFFNet). Specifically, we connect multi-scale SFF modules in parallel with a cross-scale fusion function to generate a set of cost volumes with different scales. These cost volumes are then effectively combined using the proposed interlaced concatenation method. Second, we propose an adaptive cost-volume-filtering (ACVF) loss function that directly supervises our estimated cost volume. The proposed ACVF loss directly adds constraints to the cost volume using the probability distribution generated from the ground truth disparity map and that estimated from the teacher network which achieves higher accuracy. Results of several experiments using representative datasets for stereo matching show that our proposed method is more efficient than previous methods. Our network architecture consumes fewer parameters and generates reasonable disparity maps with faster speed compared with the existing state-of-the art stereo models. Concretely, our network achieves 1.01 EPE with runtime of 42 ms, 2.92M parameters, and 97.96G FLOPs on the Scene Flow test set. Compared with PSMNet, our method is 89% faster and 7% more accurate with 45% fewer parameters.

Recent Advances in Antibody Therapeutics.

Antibody-based therapeutics have achieved unprecedented success in treating various diseases, including cancers, immune disorders, and infectious diseases [...].

Semantic Segmentation Using Pixel-Wise Adaptive Label Smoothing via Self-Knowledge Distillation for Limited Labeling Data.

To achieve high performance, most deep convolutional neural networks (DCNNs) require a significant amount of training data with ground truth labels. However, creating ground-truth labels for semantic segmentation requires more time, human effort, and cost compared with other tasks such as classification and object detection, because the ground-truth label of every pixel in an image is required. Hence, it is practically demanding to train DCNNs using a limited amount of training data for semantic segmentation. Generally, training DCNNs using a limited amount of data is problematic as it easily results in a decrease in the accuracy of the networks because of overfitting to the training data. Here, we propose a new regularization method called pixel-wise adaptive label smoothing (PALS) via self-knowledge distillation to stably train semantic segmentation networks in a practical situation, in which only a limited amount of training data is available. To mitigate the problem caused by limited training data, our method fully utilizes the internal statistics of pixels within an input image. Consequently, the proposed method generates a pixel-wise aggregated probability distribution using a similarity matrix that encodes the affinities between all pairs of pixels. To further increase the accuracy, we add one-hot encoded distributions with ground-truth labels to these aggregated distributions, and obtain our final soft labels. We demonstrate the effectiveness of our method for the Cityscapes dataset and the Pascal VOC2012 dataset using limited amounts of training data, such as 10%, 30%, 50%, and 100%. Based on various quantitative and qualitative comparisons, our method demonstrates more accurate results compared with previous methods. Specifically, for the Cityscapes test set, our method achieved mIoU improvements of 0.076%, 1.848%, 1.137%, and 1.063% for 10%, 30%, 50%, and 100% training data, respectively, compared with the method of the cross-entropy loss using one-hot encoding with ground truth labels.

Molecular basis of PD-1 blockade by dostarlimab, the FDA-approved antibody for cancer immunotherapy.

Targeting of programmed cell death 1 (PD-1) with monoclonal antibodies to block the interaction with its ligand PD-L1 has been successful in immunotherapy of multiple types of cancer, and their mechanism involves the restoration of the T-cell immune response. April 2021, the US FDA approved dostarlimab, a therapeutic antibody against PD-1, for the treatment of endometrial cancer. Here, we report the crystal structure of the extracellular domain of PD-1 in complex with the dostarlimab Fab at the resolution of 1.53 Å. Although the interaction between PD-1 and dostarlimab involves mainly the residues within the heavy chain of dostarlimab, the steric occlusion of PD-L1 binding is primarily contributed by the light chain. Dostarlimab induces conformational rearrangements of the BC, C'D and FG loops of PD-1 to achieve a high affinity. Significantly, the residue R86 within the C'D loop of PD-1 plays a critical role for dostarlimab binding by occupying the concave surface on the heavy chain via multiple interactions. This high-resolution structure can provide helpful information for designing improved anti-PD-1 biologics or effective combination strategies for cancer immunotherapy.

High-resolution structure of the vWF A1 domain in complex with caplacizumab, the first nanobody-based medicine for treating acquired TTP.

von Willebrand factor (vWF) is a huge oligomeric glycoprotein involved in blood homeostasis. However, this protein is also implicated in acquired thrombotic thrombocytopenic purpura (TTP). The blocking of its binding with platelets has been recognized as an attractive therapeutic strategy for treating acquired TTP. Caplacizumab, a bivalent single-domain antibody (VHH), is the first FDA-approved nanobody drug against vWF for the treatment of acquired TTP. Here, we describe the crystal structure of the A1 domain of vWF in complex with the caplacizumab nanobody at the resolution of 1.60 Å. This structure elucidates the precise epitope and binding mode of caplacizumab. Unexpectedly, caplacizumab binds to the bottom face of the vWF A1 domain and does not create any steric clash with platelet-receptor glycoprotein Ib (GPIb) bound to vWF. However, its binding can stabilize the different conformation within the N-terminus and α1ß2 loop from the GPIb bound structure, suggesting that the mechanisms of caplacizumab would not be the direct competition of GPIb binding to vWF A1 domain but the conformational arrestment of vWF in an inappropriate state to platelet adhesion. This high-resolution structure would provide helpful information for the design of improved anti-vWF therapeutics for the treatment of acquired TTP.

Crystal structure of CD38 in complex with daratumumab, a first-in-class anti-CD38 antibody drug for treating multiple myeloma.

Multiple myeloma is a blood cancer characterized by the plasma cell malignancy in the bone marrow, resulting in the destruction of bone tissue. Recently, the US FDA approved two antibody drugs for the treatment of multiple myeloma, daratumumab and isatuximab, targeting CD38, a type II transmembrane glycoprotein highly expressed in plasma cells and multiple myeloma cells. Here, we report the crystal structure of CD38 in complex with the Fab fragment of daratumumab, providing its exact epitope on CD38 and the structural insights into the mechanism of action of the antibody drug. Daratumumab binds to a specific discontinuous region on CD38 that includes residues located opposite to the active site of CD38. All the six complementarity determining regions of daratumumab are involved in the CD38 interaction. The epitopes of daratumumab and isatuximab do not overlap at all and their bindings to CD38 induce different structural changes within the CD38 protein. This structural study can facilitate the design of improved biologics or effective combination therapies for the treatment of multiple myeloma.

Knowledge Distillation for Semantic Segmentation Using Channel and Spatial Correlations and Adaptive Cross Entropy.

In this paper, we propose an efficient knowledge distillation method to train light networks using heavy networks for semantic segmentation. Most semantic segmentation networks that exhibit good accuracy are based on computationally expensive networks. These networks are not suitable for mobile applications using vision sensors, because computational resources are limited in these environments. In this view, knowledge distillation, which transfers knowledge from heavy networks acting as teachers to light networks as students, is suitable methodology. Although previous knowledge distillation approaches have been proven to improve the performance of student networks, most methods have some limitations. First, they tend to use only the spatial correlation of feature maps and ignore the relational information of their channels. Second, they can transfer false knowledge when the results of the teacher networks are not perfect. To address these two problems, we propose two loss functions: a channel and spatial correlation (CSC) loss function and an adaptive cross entropy (ACE) loss function. The former computes the full relationship of both the channel and spatial information in the feature map, and the latter adaptively exploits one-hot encodings using the ground truth labels and the probability maps predicted by the teacher network. To evaluate our method, we conduct experiments on scene parsing datasets: Cityscapes and Camvid. Our method presents significantly better performance than previous methods.

Crystal structure of PD-1 in complex with an antibody-drug tislelizumab used in tumor immune checkpoint therapy.

Blocking of the interaction between Programmed cell death 1 (PD-1) and its ligand PD-L1 by monoclonal antibodies has elicited unprecedented therapeutic benefits and achieved a major breakthrough in immunotherapy of multiple types of tumors. Here, we determined the crystal structure of PD-1 in complex with the Fab fragment of tislelizumab. This monoclonal antibody was approved in December 2019 by the China National Medical Product Administration for Hodgkin's lymphoma and is under multiple clinical trials in China and the US. While the three complementarity determining regions (CDRs) in the light chain are involved in the target interaction, only CDR3 within the heavy chain interacts with PD-1. Tislelizumab binds the front ß-sheet of PD-1 in a very similar way as PD-L1 binds to PD-1, thereby blocking the PD-1/PD-L1 interaction with a higher affinity. A comparative analysis of PD-1 interactions with therapeutic antibodies targeting PD-1 provides a better understanding of the blockade mechanism of PD-1/PD-L1 interaction in addition to useful information for the improvement of therapeutic antibodies capable of diminishing checkpoint signaling for cancer immunotherapy.

A small-sized protein binder specific for human PD-1 effectively suppresses the tumour growth in tumour mouse model.

Immune checkpoint inhibitors have drawn a consider attention as an effective cancer immunotherapy, and several monoclonal antibodies targeting the immune checkpoint receptors, such as human programmed cell death-1 (hPD-1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4), are clinically used for treatment of various cancers. Here we present the development of a small-sized protein binder which specifically binds to hPD-1. The protein binder, which is composed of leucine-rich repeat (LRR) modules, was selected against hPD-1 through phage display, and its binding affinity was maturated up to 17 nM by modular evolution approach. The protein binder was shown to be highly specific for hPD-1, effectively inhibiting the interaction between hPD-1 and its ligand, hPD-L1. The protein binder restored T-cell function in vitro, and exhibited a strong anti-tumour activity in tumour mouse model, indicating that it acts as an effective checkpoint blockade. Based on the results, the developed protein binder specific for hPD-1 is likely to find a potential use in cancer immunotherapy.

Orobol, an Enzyme-Convertible Product of Genistein, exerts Anti-Obesity Effects by Targeting Casein Kinase 1 Epsilon.

Soy isoflavones, particularly genistein, have been shown to exhibit anti-obesity effects. When compared with the isoflavones genistin, daidzin, coumestrol, genistein, daidzein, 6-o-dihydroxyisoflavone, equol, 3'-o-dihydroxyisoflavone, and 8-o-dihydroxyisoflavone, a remarkably higher inhibitory effect on lipid accumulation was observed for orobol treatment during adipogenesis in 3T3-L1 cells. To identify the cellular target of orobol, its pharmacological effect on 395 human kinases was analyzed. Of the 395 kinases, orobol showed the lowest half maximal inhibitory concentration (IC50) for Casein Kinase 1 epsilon (CK1ε), and bound to this target in an ATP-competitive manner. A computer modeling study revealed that orobol may potentially dock with the ATP-binding site of CK1ε via several hydrogen bonds and van der Waals interactions. The phosphorylation of eukaryotic translation initiation factor 4E-binding protein 1, a substrate of CK1ε, was inhibited by orobol in isobutylmethylxanthine, dexamethasone and insulin (MDI)-induced 3T3-L1 cells. It was also found that orobol attenuates high fat diet-induced weight gain and lipid accumulation without affecting food intake in C57BL/6J mice. These findings underline orobol's potential for development as a novel agent for the prevention and treatment of obesity.

Molecular Interactions of Antibody Drugs Targeting PD-1, PD-L1, and CTLA-4 in Immuno-Oncology.

Cancer cells can evade immune surveillance through the molecular interactions of immune checkpoint proteins, including programmed death 1 (PD-1), PD-L1, and cytotoxic T lymphocyte-associated antigen 4 (CTLA-4). Since 2011, the FDA-approved antibody drugs ipilimumab (Yervoy®), nivolumab (Opdivo®), pembrolizumab (Keytruda®), cemiplimab (Libtayo®), atezolizumab (Tecentriq®), durvalumab (Imfinzi®), and avelumab (Bavencio®), which block the immune checkpoint proteins, have brought about a significant breakthrough in the treatment of a wide range of cancers, as they can induce durable therapeutic responses. In recent years, crystal structures of the antibodies against PD-1, PD-L1, and CTLA-4 have been reported. In this review, we describe the latest structural studies of these monoclonal antibodies and their interactions with the immune checkpoint proteins. A comprehensive analysis of the interactions of these immune checkpoint blockers can provide a better understanding of their therapeutic mechanisms of action. The accumulation of these structural studies would provide a basis that is essential for the rational design of next-generation therapies in immuno-oncology.

BAFF-neutralizing interaction of belimumab related to its therapeutic efficacy for treating systemic lupus erythematosus.

BAFF, a member of the TNF superfamily, has been recognized as a good target for autoimmune diseases. Belimumab, an anti-BAFF monoclonal antibody, was approved by the FDA for use in treating systemic lupus erythematosus. However, the molecular basis of BAFF neutralization by belimumab remains unclear. Here our crystal structure of the BAFF-belimumab Fab complex shows the precise epitope and the BAFF-neutralizing mechanism of belimumab, and demonstrates that the therapeutic activity of belimumab involves not only antagonizing the BAFF-receptor interaction, but also disrupting the formation of the more active BAFF 60-mer to favor the induction of the less active BAFF trimer through interaction with the flap region of BAFF. In addition, the belimumab HCDR3 loop mimics the DxL(V/L) motif of BAFF receptors, thereby binding to BAFF in a similar manner as endogenous BAFF receptors. Our data thus provides insights for the design of new drugs targeting BAFF for the treatment of autoimmune diseases.

Structural Biology of the TNFα Antagonists Used in the Treatment of Rheumatoid Arthritis.

The binding of the tumor necrosis factor α (TNFα) to its cognate receptor initiates many immune and inflammatory processes. The drugs, etanercept (Enbrel®), infliximab (Remicade®), adalimumab (Humira®), certolizumab-pegol (Cimzia®), and golimumab (Simponi®), are anti-TNFα agents. These drugs block TNFα from interacting with its receptors and have enabled the development of breakthrough therapies for the treatment of several autoimmune inflammatory diseases, including rheumatoid arthritis, Crohn's disease, and psoriatic arthritis. In this review, we describe the latest works on the structural characterization of TNFα-TNFα antagonist interactions related to their therapeutic efficacy at the atomic level. A comprehensive comparison of the interactions of the TNFα blockers would provide a better understanding of the molecular mechanisms by which they neutralize TNFα. In addition, an enhanced understanding of the higher order complex structures and quinary structures of the TNFα antagonists can support the development of better biologics with the improved pharmacokinetic properties. Accumulation of these structural studies can provide a basis for the improvement of therapeutic agents against TNFα for the treatment of rheumatoid arthritis and other autoimmune inflammatory diseases in which TNFα plays an important role in pathogenesis.

Depressive symptoms and risk of absence among workers in a manufacturing company: a 12-month follow-up study.

Depression is a leading cause of reduced work ability and absence due to sickness. The objective of this study was to investigate how depressive symptoms are prospectively associated with subsequent absence, whether caused by illness or accidents, among manufacturing workers. This prospective study was conducted on 2,349 male and female employees that underwent a regular health examination at a university hospital. Depressive symptoms were measured at baseline using the Center for Epidemiologic Studies Depression (CES-D) Scale. Data on self-reported absence due to illness and accidents were obtained during a follow up of 1 yr. The incidences of sickness absence were 6.0% for men and 17.3% for women. Men and women with depressive symptoms (CES-D ≥16) were found to have higher odds of sickness absence during follow up (men: OR=4.06; 95% CI: 2.32-7.11; women: OR=1.75; 95% CI: 1.02-2.98), after adjustment for demographic and occupational factors. When depressive symptoms were divided into quartiles, significantly higher ORs of sickness absence were observed only among employees with the highest quartile of depressive symptoms. The study shows that depressive symptoms are a risk factor for future absence due to illness or accidents among manufacturing workers.

Molecular mechanism of PD-1/PD-L1 blockade via anti-PD-L1 antibodies atezolizumab and durvalumab.

In 2016 and 2017, monoclonal antibodies targeting PD-L1, including atezolizumab, durvalumab, and avelumab, were approved by the FDA for the treatment of multiple advanced cancers. And many other anti-PD-L1 antibodies are under clinical trials. Recently, the crystal structures of PD-L1 in complex with BMS-936559 and avelumab have been determined, revealing details of the antigen-antibody interactions. However, it is still unknown how atezolizumab and durvalumab specifically recognize PD-L1, although this is important for investigating novel binding sites on PD-L1 targeted by other therapeutic antibodies for the design and improvement of anti-PD-L1 agents. Here, we report the crystal structures of PD-L1 in complex with atezolizumab and durvalumab to elucidate the precise epitopes involved and the structural basis for PD-1/PD-L1 blockade by these antibodies. A comprehensive comparison of PD-L1 interactions with anti-PD-L1 antibodies provides a better understanding of the mechanism of PD-L1 blockade as well as new insights into the rational design of improved anti-PD-L1 therapeutics.

Osajin Inhibits Solar UV-Induced Cyclooxygenase-2 Expression Through Direct Inhibition of RSK2.

Solar ultraviolet light (sUV) has been shown to promote the development of skin disorders including inflammation, photoaging, and skin carcinogenesis. Osajin is the major bioactive isoflavone present in the fruit of Maclura pomifera, commonly referred to as the Osage orange. In this study, we observed that osajin inhibited sUV-induced cyclooxygenase (COX)-2 protein expression in both HaCaT and JB6 cells. COX-2 is a major mediator of skin inflammation. sUV activated the transcription factors nuclear factor-κB and activator protein-1 which, in turn, induces COX-2 expression. Osajin inhibited transactivation of these transcription factors. We identified RSK2 as an inhibitory target of osajin by screening against 68 kinases related to inflammation. Osajin binds with RSK2 directly in an ATP-competitive manner. Computer modeling simulated a plausible binding orientation between osajin and RSK2. Osajin inhibited sUV-induced phosphorylation of histone H3, a substrate of RSK2. However, sUV-induced phosphorylation of extracellular signal-regulated kinases, p38 kinase, c-Jun N-terminal kinase and Akt, which are signaling factors upstream of RSK2, was unchanged in the presence of osajin. The anti-inflammatory effects and molecular mechanism of osajin suggest that it may have utility as a functional food for skin health and cosmetic ingredient. J. Cell. Biochem. 118: 4080-4087, 2017. © 2017 Wiley Periodicals, Inc.