An adaptive multi-modal hybrid model for classifying thyroid nodules by combining ultrasound and infrared thermal images.

BACKGROUND: Two types of non-invasive, radiation-free, and inexpensive imaging technologies that are widely employed in medical applications are ultrasound (US) and infrared thermography (IRT). The ultrasound image obtained by ultrasound imaging primarily expresses the size, shape, contour boundary, echo, and other morphological information of the lesion, while the infrared thermal image obtained by infrared thermography imaging primarily describes its thermodynamic function information. Although distinguishing between benign and malignant thyroid nodules requires both morphological and functional information, present deep learning models are only based on US images, making it possible that some malignant nodules with insignificant morphological changes but significant functional changes will go undetected. RESULTS: Given the US and IRT images present thyroid nodules through distinct modalities, we proposed an Adaptive multi-modal Hybrid (AmmH) classification model that can leverage the amalgamation of these two image types to achieve superior classification performance. The AmmH approach involves the construction of a hybrid single-modal encoder module for each modal data, which facilitates the extraction of both local and global features by integrating a CNN module and a Transformer module. The extracted features from the two modalities are then weighted adaptively using an adaptive modality-weight generation network and fused using an adaptive cross-modal encoder module. The fused features are subsequently utilized for the classification of thyroid nodules through the use of MLP. On the collected dataset, our AmmH model respectively achieved 97.17% and 97.38% of F1 and F2 scores, which significantly outperformed the single-modal models. The results of four ablation experiments further show the superiority of our proposed method. CONCLUSIONS: The proposed multi-modal model extracts features from various modal images, thereby enhancing the comprehensiveness of thyroid nodules descriptions. The adaptive modality-weight generation network enables adaptive attention to different modalities, facilitating the fusion of features using adaptive weights through the adaptive cross-modal encoder. Consequently, the model has demonstrated promising classification performance, indicating its potential as a non-invasive, radiation-free, and cost-effective screening tool for distinguishing between benign and malignant thyroid nodules. The source code is available at https://github.com/wuliZN2020/AmmH .

High-precision surface profilometry on a micron-groove based on dual-comb electronically controlled optical sampling.

We demonstrate an optical method for 3D profilometry of micro-nano devices with large step structures. The measurement principle is based on a dual-comb direct time-of-flight detection. An electronically controlled optical sampling (ECOPS) approach is used to improve the acquisition rate. In a proof-of-principle distance measurement experiment, the measurement precision reaches 15 nm at 4000-times averages. The method has been used to characterize the profile of a large aspect-ratio rectangular micron-groove with 10 µm width and 62.3 µm depth. By point-by-point scanning, a 3D point cloud image is obtained, and the 3D profile of the micro-structure is quantitatively reconstructed with sub-micrometer precision. The proposed high-precision, high-speed surface 3D profile measurement technology could be applied to profilometry and inspection of complex microelectronics devices in the future.

Physics-informed neural network for phase imaging based on transport of intensity equation.

Non-interferometric quantitative phase imaging based on Transport of Intensity Equation (TIE) has been widely used in bio-medical imaging. However, analytic TIE phase retrieval is prone to low-spatial frequency noise amplification, which is caused by the illposedness of inversion at the origin of the spectrum. There are also retrieval ambiguities resulting from the lack of sensitivity to the curl component of the Poynting vector occurring with strong absorption. Here, we establish a physics-informed neural network (PINN) to address these issues, by integrating the forward and inverse physics models into a cascaded deep neural network. We demonstrate that the proposed PINN is efficiently trained using a small set of sample data, enabling the conversion of noise-corrupted 2-shot TIE phase retrievals to high quality phase images under partially coherent LED illumination. The efficacy of the proposed approach is demonstrated by both simulation using a standard image database and experiment using human buccal epitehlial cells. In particular, high image quality (SSIM = 0.919) is achieved experimentally using a reduced size of labeled data (140 image pairs). We discuss the robustness of the proposed approach against insufficient training data, and demonstrate that the parallel architecture of PINN is efficient for transfer learning.

High speed time-of-flight displacement measurement based on dual-comb electronically controlled optical sampling.

We demonstrate a direct time-of-flight approach that utilizes dual-comb electronically controlled optical sampling (ECOPS) to measure small displacements. ECOPS is enabled by electrically controlling the repetition rate of one laser via an intracavity electric-optical modulator (EOM). The acquisition rate is set by the EOM modulation frequency, which is much higher than commonly used asynchronous optical sampling (ASOPS). In a proof-of-principle experiment, an 80-kHz acquisition rate is obtained with a pair of ∼105 MHz repetition rate Er-fiber lasers. At an average time of 30 ms, a measurement precision evaluated with Allan deviation reaches 26.1 nm for a 40-µm static displacement. In a dynamic measurement, a 500-Hz sinusoidal vibration with 15 µm amplitude has also been identified. The high-precision and high-speed displacement measurement technique can be potentially used in 3D surface profilometry of microelectronic step-structures and real-time monitoring of high frequency mechanical vibrations, etc.

Clinical characteristics and outcomes of COVID-19 patients with gastrointestinal symptoms admitted to Jianghan Fangcang Shelter Hospital in Wuhan, China.

Coronavirus disease 2019 (COVID-19) is a health emergency worldwide, and gastrointestinal (GI) symptoms are increasingly reported in COVID-19 patients. However, sample size was small and the incidence of GI symptoms in patients was variable across studies, and the correlation between these symptoms and clinical outcomes remains incompletely understood. The objective of this study is to compare clinical characteristics and outcomes between patients with and without GI symptoms admitted to Jianghan Fangcang Shelter Hospital in Wuhan. This retrospective study recruited 1320 COVID-19 patients admitted to hospital from 5 February 2020 to 9 March 2020. On the basis of the presence of GI symptoms, the sample was divided into a GI group (n = 192) and a non-GI group (n = 1128). The three most common GI symptoms were diarrhea (8.1%), anorexia (4.7%), and nausea and vomiting (4.3%). The rate of clinical deterioration was significantly higher in the GI group than in the non-GI group (15.6% vs. 10.1%, P = .032). GI symptoms (P = .045), male gender P < .001), and increased C-reactive protein (P = .008) were independent risk factors for clinical worsening. This study demonstrated that the rate of clinical deterioration was significantly higher in the GI group. Furthermore, potential risk factors for developing GI symptoms, male gender, and increased C-reactive protein can help clinicians predict clinical outcomes in COVID-19 patients.

Stability Analysis of Multi Process Parameters for Metal-Organic Chemical Vapor Deposition Reaction Cavity.

The parameters for metal-organic chemical vapor deposition (MOCVD) processes significantly influence the properties of ZnO films, especially the flow stability of the chamber, which is caused by process parameters such as the shape of reaction chamber, the working pressure, the growth temperature, the susceptor rotational speed, the gas flow rate, and the nature of the carrier gas at inlet temperature. These parameters are the preconditions for the formation of high-quality film. Therefore, this study uses Ar as a carrier gas, diethylzinc (DEZn) as a Zn source, and H2O as an oxygen source and adopts the reaction mechanism calculated by quantum chemistry, which includes ten gas reactions and eight surface reactions. The process parameters of a specific reaction chamber model were analyzed based on the computational fluid dynamics method. This study also presents an accurate prediction of the flow regime in the reactor chamber under any operating conditions, without additional experiments, based on an analysis of a great quantity of simulation data. Such research is also significant for selecting the growth parameters relevant to production, providing a specific process growth window, narrowing the debugging scope, and providing a theoretical basis for the development of MOCVD equipment and process debugging.

Superfast Zincophilic Ion Conductor Enables Rapid Interfacial Desolvation Kinetics for Low-Temperature Zinc Metal Batteries.

Low-temperature rechargeable aqueous zinc metal batteries (AZMBs) as highly promising candidates for energy storage are largely hindered by huge desolvation energy barriers and depressive Zn2+ migration kinetics. In this work, a superfast zincophilic ion conductor of layered zinc silicate nanosheet (LZS) is constructed on a metallic Zn surface, as an artificial layer and ion diffusion accelerator. The experimental and simulation results reveal the zincophilic ability and layer structure of LZS not only promote the desolvation kinetics of [Zn(H2O)6]2+ but also accelerate the Zn2+ transport kinetics across the anode/electrolyte interface, guiding uniform Zn deposition. Benefiting from these features, the LZS-modified Zn anodes showcase long-time stability (over 3300 h) and high Coulombic efficiency with ≈99.8% at 2 mA cm-2, respectively. Even reducing the environment temperature down to 0 °C, ultralong cycling stability up to 3600 h and a distinguished rate performance are realized. Consequently, the assembled Zn@LZS//V2O5-x full cells deliver superior cyclic stability (344.5 mAh g-1 after 200 cycles at 1 A g-1) and rate capability (285.3 mAh g-1 at 10 A g-1) together with a low self-discharge rate, highlighting the bright future of low-temperature AZMBs.

Attentional Ptycho-Tomography (APT) for three-dimensional nanoscale X-ray imaging with minimal data acquisition and computation time.

Noninvasive X-ray imaging of nanoscale three-dimensional objects, such as integrated circuits (ICs), generally requires two types of scanning: ptychographic, which is translational and returns estimates of the complex electromagnetic field through the IC; combined with a tomographic scan, which collects these complex field projections from multiple angles. Here, we present Attentional Ptycho-Tomography (APT), an approach to drastically reduce the amount of angular scanning, and thus the total acquisition time. APT is machine learning-based, utilizing axial self-Attention for Ptycho-Tomographic reconstruction. APT is trained to obtain accurate reconstructions of the ICs, despite the incompleteness of the measurements. The training process includes regularizing priors in the form of typical patterns found in IC interiors, and the physics of X-ray propagation through the IC. We show that APT with ×12 reduced angles achieves fidelity comparable to the gold standard Simultaneous Algebraic Reconstruction Technique (SART) with the original set of angles. When using the same set of reduced angles, then APT also outperforms Filtered Back Projection (FBP), Simultaneous Iterative Reconstruction Technique (SIRT) and SART. The time needed to compute the reconstruction is also reduced, because the trained neural network is a forward operation, unlike the iterative nature of these alternatives. Our experiments show that, without loss in quality, for a 4.48 × 93.2 × 3.92 µm3 IC (≃6 × 108 voxels), APT reduces the total data acquisition and computation time from 67.96 h to 38 min. We expect our physics-assisted and attention-utilizing machine learning framework to be applicable to other branches of nanoscale imaging, including materials science and biological imaging.

Data Mining Method of Intelligent Market Management Based on Collaborative Recommendation Algorithm.

In order to solve the problems of low coverage and accuracy and large mean absolute error and root mean square error when traditional algorithms recommend market management data, this paper proposes an intelligent market management data mining method based on a collaborative recommendation algorithm. According to the preference value of the attribute characteristics of market management data, predict and score the attribute characteristics of market management data; use data mining technology to preprocess the information of market management data, combined with the design of collaborative filtering recommendation algorithm; and realize the collaborative filtering recommendation of market management data. With 50 recommendations, AGCAN improves the accuracy of MovieLens-1M by 43.81%, 5.43%, 1.87%, 0.42%, and 1.67%, respectively, compared with the five benchmark algorithms. For MovieLens-100K, compared with the five benchmark algorithms, AGCAN improves the accuracy by 51.17%, 10.52%, 3.37%, 0.1%, and 0.30%, respectively. Compared with the five benchmark algorithms, Amazon-baby and AGCAN have improved the accuracy by 34.37%, 28.12%, 31.25%, 29.1%, and 3.12%, respectively. The algorithm proposed in this paper uses a graph neural network to mine useful information between users and projects, but it lacks the use of other personalized interest information of users, such as user interest, user purchase time, and so on.

High strength and damage-tolerance in echinoderm stereom as a natural bicontinuous ceramic cellular solid.

Due to their low damage tolerance, engineering ceramic foams are often limited to non-structural usages. In this work, we report that stereom, a bioceramic cellular solid (relative density, 0.2-0.4) commonly found in the mineralized skeletal elements of echinoderms (e.g., sea urchin spines), achieves simultaneous high relative strength which approaches the Suquet bound and remarkable energy absorption capability (ca. 17.7 kJ kg-1) through its unique bicontinuous open-cell foam-like microstructure. The high strength is due to the ultra-low stress concentrations within the stereom during loading, resulted from their defect-free cellular morphologies with near-constant surface mean curvatures and negative Gaussian curvatures. Furthermore, the combination of bending-induced microfracture of branches and subsequent local jamming of fractured fragments facilitated by small throat openings in stereom leads to the progressive formation and growth of damage bands with significant microscopic densification of fragments, and consequently, contributes to stereom's exceptionally high damage tolerance.

P-Doped NiTe2 with Te-Vacancies in Lithium-Sulfur Batteries Prevents Shuttling and Promotes Polysulfide Conversion.

Lithium-sulfur (Li-S) batteries have been hindered by the shuttle effect and sluggish polysulfide conversion kinetics. Here, a P-doped nickel tellurium electrocatalyst with Te-vacancies (P⊂NiTe2- x ) anchored on maize-straw carbon (MSC) nanosheets, served as a functional layer (MSC/P⊂NiTe2- x ) on the separator of high-performance Li-S batteries. The P⊂NiTe2- x electrocatalyst enhanced the intrinsic conductivity, strengthened the chemical affinity for polysulfides, and accelerated sulfur redox conversion. The MSC nanosheets enabled NiTe2 nanoparticle dispersion and Li+ diffusion. In situ Raman and ex situ X-ray absorption spectra confirmed that the MSC/P⊂NiTe2- x restrained the shuttle effect and accelerated the redox conversion. The MSC/P⊂NiTe2- x -based cell has a cyclability of 637 mAh g-1 at 4 C over 1800 cycles with a degradation rate of 0.0139% per cycle, high rate performance of 726 mAh g-1 at 6 C, and a high areal capacity of 8.47 mAh cm-2 under a sulfur configuration of 10.2 mg cm-2 , and a low electrolyte/sulfur usage ratio of 3.9. This work demonstrates that vacancy-induced doping of heterogeneous atoms enables durable sulfur electrochemistry and can impact future electrocatalytic designs related to various energy-storage applications.

Src Activation Aggravates Podocyte Injury in Diabetic Nephropathy via Suppression of FUNDC1-Mediated Mitophagy.

Background and purpose: Mitophagy plays a significant role in the progression of diabetic nephropathy (DN), although the regulatory mechanisms remain unclear. Recently, accumulating evidence demonstrated that impaired mitochondrial function and mitophagy are involved in DN. Here, we are aimed to explore the role of c-Src (Src) and FUNDC1-related mitophagy in the development of DN. Methods: The db/db mice were used to establish a DN mice model. The mice accepted PP2 (Src inhibitor) treatment to study the role of Src in DN. Kidney function was measured via biochemical testing. Renal histopathology and morphometric analysis were conducted via hematoxylin-eosin (HE), periodic acid-Schiff (PAS), Masson's staining, and transmission electron microscopy (TEM). We measured degree of apoptosis in kidney by TUNEL assay. Indices of mitophagy (LC3 and p62) were evaluated by Western blotting and immunofluorescence. Complementary in vitro assays were conducted using human podocytes subjected to high glucose in combination with PP2 treatment or FUNDC1 small interfering RNAs (siRNAs). Flow cytometry was used to detect the apoptotic cells. Mitochondrial function was evaluated by JC-1 staining. Double immunofluorescence labeling of LC3 and TOMM20 used to assess the degree of mitophagy. Results: Increased Src activation was detected in the kidneys of db/db mice, and its expression was positively correlated with mitochondrial damage, podocyte apoptosis, and renal dysfunction. Inhibition of Src activation with PP2 protected against mitochondrial damage and podocyte apoptosis. In vitro experiments in podocytes established that high glucose increased Src activation, promoting FUNDC1 phosphorylation and inhibiting mitophagy. Consistent with the mouse model, inhibiting Src activity protected podocytes against mitochondrial damage. FUNDC1 silencing negated the actions of PP2, indicating that FUNDC1-mediated mitophagy is downstream pathway of Src. Conclusion: In summary, our data indicated that Src is a culprit factor in diabetic renal damage via suppression of FUNDC1-mediated mitophagy, promoting the development of DN.

Dynamic Intercalation-Conversion Site Supported Ultrathin 2D Mesoporous SnO2/SnSe2 Hybrid as Bifunctional Polysulfide Immobilizer and Lithium Regulator for Lithium-Sulfur Chemistry.

The practical application of lithium-sulfur batteries is impeded by the polysulfide shuttling and interfacial instability of the metallic lithium anode. In this work, a twinborn ultrathin two-dimensional graphene-based mesoporous SnO2/SnSe2 hybrid (denoted as G-mSnO2/SnSe2) is constructed as a polysulfide immobilizer and lithium regulator for Li-S chemistry. The as-designed G-mSnO2/SnSe2 hybrid possesses high conductivity, strong chemical affinity (SnO2), and a dynamic intercalation-conversion site (LixSnSe2), inhibits shuttle behavior, provides rapid Li-intercalative transport kinetics, accelerates LiPS conversion, and decreases the decomposition energy barrier for Li2S, which is evidenced by the ex situ XAS spectra, in situ Raman, in situ XRD, and DFT calculations. Moreover, the mesoporous G-mSnO2/SnSe2 with lithiophilic characteristics enables homogeneous Li-ion deposition and inhibits Li dendrite growth. Therefore, Li-S batteries with a G-mSnO2/SnSe2 separator achieve a favorable electrochemical performance, including high sulfur utilization (1544 mAh g-1 at 0.2 C), high-rate capability (794 mAh g-1 at 8 C), and long cycle life (extremely low attenuation rate of 0.0144% each cycle at 5 C over 2000 cycles). Encouragingly, a 1.6 g S/Ah-level pouch cell realizes a high energy density of up to 359 Wh kg-1 under a lean E/S usage of 3.0 µL mg-1. This work sheds light on the design roadmap for tackling S-cathode and Li-anode challenges simultaneously toward long-durability Li-S chemistry.

Small RNAs, Degradome, and Transcriptome Sequencing Provide Insights into Papaya Fruit Ripening Regulated by 1-MCP.

As an inhibitor of ethylene receptors, 1-methylcyclopropene (1-MCP) can delay the ripening of papaya. However, improper 1-MCP treatment will cause a rubbery texture in papaya. Understanding of the underlying mechanism is still lacking. In the present work, a comparative sRNA analysis was conducted after different 1-MCP treatments and identified a total of 213 miRNAs, of which 44 were known miRNAs and 169 were novel miRNAs in papaya. Comprehensive functional enrichment analysis indicated that plant hormone signal pathways play an important role in fruit ripening. Through the comparative analysis of sRNAs and transcriptome sequencing, a total of 11 miRNAs and 12 target genes were associated with the ethylene and auxin signaling pathways. A total of 1741 target genes of miRNAs were identified by degradome sequencing, and nine miRNAs and eight miRNAs were differentially expressed under the ethylene and auxin signaling pathways, respectively. The network regulation diagram of miRNAs and target genes during fruit ripening was drawn. The expression of 11 miRNAs and 12 target genes was verified by RT-qPCR. The target gene verification showed that cpa-miR390a and cpa-miR396 target CpARF19-like and CpERF RAP2-12-like, respectively, affecting the ethylene and auxin signaling pathways and, therefore, papaya ripening.

Quantitative 3D structural analysis of the cellular microstructure of sea urchin spines (I): Methodology.

The mineralized skeletons of echinoderms are characterized by their complex, open-cell porous microstructure (also known as stereom), which exhibits vast variations in pore sizes, branch morphology, and three-dimensional (3D) organization patterns among different species. Quantitative description and analysis of these cellular structures in 3D are needed in order to understand their mechanical properties and underlying design strategies. In this paper series, we present a framework for analyzing such structures based on high-resolution 3D tomography data and utilize this framework to investigate the structural designs of stereom by using the spines from the sea urchin Heterocentrotus mamillatus as a model system. The first paper here reports the proposed cellular network analysis framework, which consists of five major steps: synchrotron-based tomography and hierarchical convolutional neural network-based reconstruction, machine learning-based segmentation, cellular network registration, feature extraction, and data representation and analysis. This framework enables the characterization of the porous stereom structures at the individual node and branch level (~10 µm), the local cellular level (~100 µm), and the global network level (~1 mm). We define and quantify multiple structural descriptors at each level, such as node connectivity, branch length and orientation, branch profile, ring structure, etc., which allows us to investigate the cellular network construction of H. mamillatus spines quantitatively. The methodology reported here could be tailored to analyze other natural or engineering open-cell porous materials for a comprehensive multiscale network representation and mechanical analysis. STATEMENT OF SIGNIFICANCE: The mechanical robustness of the biomineralized porous structures in sea urchin spines has long been recognized. However, quantitative cellular network representation and analysis of this class of natural cellular solids are still limited in the literature. This constrains our capability to fully understand the mechanical properties and design strategies in sea urchin spines and other similar echinoderms' porous skeletal structures. Combining high-resolution tomography and computer vision-based analysis, this work presents a multiscale 3D network analysis framework, which allows for extraction, registration, and quantification of sea urchin spines' complex porous structure from the individual branch and node level to the global network level. This 3D structural analysis is relevant to a diversity of research fields, such as biomineralization, skeletal biology, biomimetics, material science, etc.

Quantitative 3D structural analysis of the cellular microstructure of sea urchin spines (II): Large-volume structural analysis.

Biological cellular materials have been a valuable source of inspiration for the design of lightweight engineering structures. In this process, a quantitative understanding of the biological cellular materials from the individual branch and node level to the global network level in 3D is required. Here we adopt a multiscale cellular network analysis workflow demonstrated in the first paper of this work series to analyze the biomineralized porous structure of sea urchin spines from the species Heterocentrotus mamillatus over a large volume (ca. 0.32mm3). A comprehensive set of structural descriptors is utilized to quantitatively delineate the long-range microstructural variation from the spine center to the edge region. Our analysis shows that the branches gradually elongate (~50% increase) and thicken (~100% increase) from the spine center to edge, which dictates the spatial variation of relative density (from ~12% to ~40%). The branch morphology and network organization patterns also vary gradually with their positions and orientations. Additionally, the analysis of the cellular network of individual septa provides the interconnection characteristics between adjacent septa, which are the primary structural motifs used for the construction of the cellular structure in the edge region. Lastly, combining the extracted long-range cellular network and finite element simulations allows us to efficiently examine the spatial and orientational dependence of local effective Young's modulus across the spine's radius. The structural-mechanical analysis here sheds light on the structural designs of H. mamillatus' porous spines, which could provide important insights for the design and modeling of lightweight yet strong and damage-tolerant cellular materials. STATEMENT OF SIGNIFICANCE: Previous investigations on the cellular structures of sea urchin spines have been mainly based on 2D measurements or 3D quantification of small volumes with limited structural parameters. This limits our understanding of the interplay between the 3D microstructural variations and the mechanical properties in sea urchin spines, which hence constrains the derivation of the underlying principles for bio-inspired designs. This work utilizes our multiscale 3D network analysis, for the first time, to quantify the 3D cellular network and its variation across large volumes in sea urchin spines from individual branch and node level to the cellular network level. The network analysis demonstrated here is expected to be of great interest to the fields of biomineralization, functional biological materials, and bio-inspired material design.

Clinical characteristics and prognostic factors of COVID-19 patients progression to severe: a retrospective, observational study.

The outbreak of coronavirus disease 2019 (COVID-19) has become a world-wide emergency. The severity of COVID-19 is highly correlated with its mortality rate. We aimed to disclose the clinical characteristics and prognostic factors of COVID-19 patients who developed severe COVID-19. The study enrolled cases (no=1848) with mild or moderate type of COVID-19 in Fangcang shelter hospital of Jianghan. A total of 56 patients progressed from mild or moderate to severe. We used least absolute shrinkage and selection operator regression model to select prognostic factors for this model. The case-severity rate was 3.6% in the shelter hospital. They were all symptomatic at admission. Fever, cough, and fatigue were the most common symptoms. Hypertension, diabetes and coronary heart diseases were common co-morbidities. Predictors contained in the prediction nomogram included fever, distribution of peak temperature (>38°C), myalgia or arthralgia and distribution of C-reactive protein (≥10 mg per L). The distribution of peak temperature (>38°C) on set, myalgia or arthralgia and C-reactive protein (≥10 mg per L) were the prognostic factors to identify the progression of COVID-19 patients with mild or moderate type. Early attention to these risk factors will help alleviate the progress of the COVID-19.

Fabrication of Zein/Pectin Hybrid Particle-Stabilized Pickering High Internal Phase Emulsions with Robust and Ordered Interface Architecture.

Diets containing partially hydrogenated oils (PHOs) expose the human body to trans fatty acids, thus endangering cardiovascular health. Pickering high internal phase emulsions (HIPEs) is a promising alternative of PHOs. This work attempted to construct stable Pickering HIPEs by engineering interface architecture through manipulating the interfacial, self-assembly, and packing behavior of zein particles using the interaction between protein and pectin. Partially wettable zein/pectin hybrid particles (ZPHPs) with three-phase contact angles ranging from 84° to 87° were developed successfully. ZPHPs were irreversibly anchored at the oil-water interface, resulting in robust and ordered interfacial structure, evidenced by the combination of LB-SEM and CLSM. This situation helped to hold a percolating 3D oil droplet network, which facilitated the formation of Pickering HIPEs with viscoelasticity, excellent thixotropy (>91.0%), and storage stability. Curcumin in HIPEs was well protected from UV-induced degradation and endowed HIPEs with ideal oxidant stability. Fabricated Pickering HIPEs possess a charming application prospect in foods and the pharmaceutical industry.

Development of antioxidant Pickering high internal phase emulsions (HIPEs) stabilized by protein/polysaccharide hybrid particles as potential alternative for PHOs.

We report for the first time the usage of mono-dispersed gliadin/chitosan hybrid particles as a particulate emulsifier for Pickering high internal phase emulsions (HIPEs) development. The hybrid particles with partial wettability were fabricated at pH 5.0 using a facile anti-solvent route. Stable Pickering HIPEs with internal phases of up to 83% can be prepared with low particle concentrations (0.5-2%). The hybrid latexes were effectively adsorbed and anchored at the oil-water interface to exert steric hindrance against coalescence. Concomitantly, the compressed droplets in Pickering HIPEs to form a percolating 3D-network framework endowed the emulsions viscoelastic and self-standing features. The protective effect of Pickering HIPEs on curcumin was confirmed, and the content of primary oxidation products in HIPEs was slightly lower than that in bulk oil. This work opens an attractive strategy to convert liquid oils to viscoelastic soft solids without artificial trans fats, as a potential alternative for PHOs.