Irregular Shape Research Articles

Nomogram prediction of vessels encapsulating tumor clusters in small hepatocellular carcinoma ≤ 3 cm based on enhanced magnetic resonance imaging

BACKGROUND Vessels encapsulating tumor clusters (VETC) represent a recently discovered vascular pattern associated with novel metastasis mechanisms in hepatocellular carcinoma (HCC). However, it seems that no one have focused on predicting VETC status in small HCC (sHCC). This study aimed to develop a new nomogram for predicting VETC positivity using preoperative clinical data and image features in sHCC (≤ 3 cm) patients. AIM To construct a nomogram that combines preoperative clinical parameters and image features to predict patterns of VETC and evaluate the prognosis of sHCC patients. METHODS A total of 309 patients with sHCC, who underwent segmental resection and had their VETC status confirmed, were included in the study. These patients were recruited from three different hospitals: Hospital 1 contributed 177 patients for the training set, Hospital 2 provided 78 patients for the test set, and Hospital 3 provided 54 patients for the validation set. Independent predictors of VETC were identified through univariate and multivariate logistic analyses. These independent predictors were then used to construct a VETC prediction model for sHCC. The model’s performance was evaluated using the area under the curve (AUC), calibration curve, and clinical decision curve. Additionally, Kaplan-Meier survival analysis was performed to confirm whether the predicted VETC status by the model is associated with early recurrence, just as it is with the actual VETC status and early recurrence. RESULTS Alpha-fetoprotein_lg10, carbohydrate antigen 199, irregular shape, non-smooth margin, and arterial peritumoral enhancement were identified as independent predictors of VETC. The model incorporating these predictors demonstrated strong predictive performance. The AUC was 0.811 for the training set, 0.800 for the test set, and 0.791 for the validation set. The calibration curve indicated that the predicted probability was consistent with the actual VETC status in all three sets. Furthermore, the decision curve analysis demonstrated the clinical benefits of our model for patients with sHCC. Finally, early recurrence was more likely to occur in the VETC-positive group compared to the VETC-negative group, regardless of whether considering the actual or predicted VETC status. CONCLUSION Our novel prediction model demonstrates strong performance in predicting VETC positivity in sHCC (≤ 3 cm) patients, and it holds potential for predicting early recurrence. This model equips clinicians with valuable information to make informed clinical treatment decisions.

Manipulation of large, irregular-shape particles using contour-tracking optical tweezers.

While the optical tweezers technique is a promising tool for manipulation of microparticles, its application to large (>50 µm) particles and irregular-shape ones is still a hard task. In this Letter, we propose what is to our knowledge a novel concept of contour-tracking optical tweezers (CTOTs), which extract the contour of the objective particle to form the illumination pattern of the trapping laser into the contour shape in real time. We demonstrated the trapping of polystyrene particles of irregular shape with the size of over 100 µm with CTOTs. Our approach has potential to open the way for expanding the applicability of optical tweezers by enabling manipulation of a variety of samples.

Role of Ripening in the Deposition of Fragments: The Case of Micro- and Nanoplastics.

Particulate contaminants, such as microplastics (1 μm to 5 mm) and nanoplastics (<1 μm), are disseminated in many terrestrial environments. However, it is still unclear how particles' properties drive their mobility through soils and aquifers due to (i) poor environmental relevance of the model particles that are studied (e.g., spherical and monodisperse) and (ii) the use of packed bed experiments which do not allow a direct observation of deposition dynamics. Using transparent 2D porous media, this study analyzes deposition dynamics of rough polystyrene fragments with irregular shapes and with a size continuum (≈10 nm to 5 μm). Using in situ and ex situ measurements, particle deposition as a function of size was monitored over time under repulsive conditions. In the absence of natural organic matter (NOM), micrometric particles rapidly deposit and promote the physical interception of smaller nanoparticles by creating local porous roughness or obstacles. In the presence of NOM, differences according to particle size were no longer observed, and all fragments were more prone to being re-entrained, thereby limiting the growth of deposits. This work demonstrates the importance of pore surface roughness and porosity of the pore surface for the deposition of colloidal particles, such as microplastics and nanoplastics, under repulsive conditions.

Investigating the Influence of Varied Particle Sizes on the Load-Bearing Properties of Arrester Bed Aggregates

This study employs the discrete element method to investigate the influence of particle size on the load-bearing characteristics of aggregates, with a specific emphasis on the aggregates used in escape ramp arrester beds. This study utilises the log edge detection algorithm to introduce an innovative approach for modelling irregularly shaped pebbles, integrating their physical properties into a comprehensive discrete element model to enhance the accuracy and applicability of simulations involving such pebbles. Meticulous validation and parameter calibration (friction coefficient: 0.37, maximum RMSE: 3.43) confirm the accuracy of the simulations and facilitate an in-depth examination of the mechanical interactions between aggregate particles at macroscopic and microscopic scales. The findings reveal a significant relationship between the particle size and load-bearing capacity of aggregates. Smaller pebbles, which are more flexible under pressure, can be packed more densely, thereby improving the distribution of vertical forces and increasing the concentration of local stress. This enhancement substantially increases the overall load-bearing capacity of aggregates. These discoveries hold significant implications for engineering practices, particularly in the optimisation of safety for truck escape ramps and in identifying the ideal sizes of pebbles with irregular shapes.

Structural, vibrational and optical characterisations of Li2Ba3(P2O7)2:Cr3+ by solid-state reaction method

ABSTRACT Cr3+ doped Li2Ba3(P2O7)2 pyrophosphate nanopowder was successfully synthesised by solid-state reaction technique. The nanopowder was characterised by several techniques. X-ray diffraction analysis reveals orthorhombic crystal structureand average crystallite size values 34.21 and 28.41 nm are determined by Debye Scherrer’s formula and Williamson-Hall (W-H) method. The morphology displayed both SEM and HR-TEM images are irregular stone-like shapes. EDS analysis confirms the constituted elements. The SAED patterns of HR-TEM with well matched with XRD data. FT-IR and Raman spectra analysis confirmed incorporation of Cr3+ in the Li2Ba3(P2O7)2 pyrophosphate nanopowder. The EPR spectrum exhibited signal at g = 1.968 which is attributed to interaction between Cr3+ and Cr3+ exchange pair. The site symmetry of Cr3+ ions occupy a distorted octahedral. Photoluminescence spectrum exhibited bands correspond to ultraviolet, blue and green regions. The evaluated CCT of 6517 K indicates yellowish-green emission near cold light which reveals that the present prepared sample is useful for LEDs applications.

Automatic classification and segmentation of blast cells using deep transfer learning and active contours.

Acute lymphoblastic leukemia (ALL) presents a formidable challenge in hematological malignancies, necessitating swift and precise diagnostic techniques for effective intervention. The conventional manual microscopy of blood smears, although widely practiced, suffers from significant limitations including labor-intensity and susceptibility to human error, particularly in distinguishing the subtle differences between normal and leukemic cells. To overcome these limitations, our research introduces the ALLDet classifier, an innovative tool employing deep transfer learning for the automated analysis and categorization of ALL from White Blood Cell (WBC) nuclei images. Our investigation encompassed the evaluation of nine state-of-the-art pre-trained convolutional neural network (CNN) models, namely VGG16, VGG19, ResNet50, ResNet101, DenseNet121, DenseNet201, Xception, MobileNet, and EfficientNetB3. We augmented this approach by incorporating a sophisticated contour-based segmentation technique, derived from the Chan-Vese model, aimed at the meticulous segmentation of blast cell nuclei in blood smear images, thereby enhancing the accuracy of our analysis. The empirical assessment of these methodologies underscored the superior performance of the EfficientNetB3 model, which demonstrated exceptional metrics: a recall specificity of 98.5%, precision of 95.86%, F1-score of 97.16%, and an overall accuracy rate of 97.13%. The Chan-Vese model's adaptability to the irregular shapes of blast cells and its noise-resistant segmentation capability were key to capturing the complex morphological changes essential for accurate segmentation. The combined application of the ALLDet classifier, powered by EfficientNetB3, with our advanced segmentation approach, emerges as a formidable advancement in the early detection and accurate diagnosis of ALL. This breakthrough not only signifies a pivotal leap in leukemia diagnostic methodologies but also holds the promise of significantly elevating the standards of patient care through the provision of timely and precise diagnoses. The implications of this study extend beyond immediate clinical utility, paving the way for future research to further refine and enhance the capabilities of artificial intelligence in medical diagnostics.

Real-time deep-learning-based object detection and unsupervised statistical analysis for quantitative evaluation of defect length direction on magnetooptical faraday effect

Magnetooptical nondestructive inspection (MONDI) is widely used in various industries to detect defects in metal including ferromagnetic materials. Despite its high precision and sensitivity, its inspection accuracy is highly dependent on the direction of the magnetizer. In addition, the irregular shapes of defects make the assessment of their severity challenging. To address these limitations, we propose an efficient real-time deep learning (DL)-based object detection model called the MONDI detector. This model is designed to integrate a DL-based object detection model with statistical analysis based on regression model to classify and quantify the magnetizer direction. To comprehensively assess algorithm performance, this study investigates variations in the excitation field, defect shape, and defect depth. This analysis is expected to contribute to the state-of-the-art object detection paradigm in MONDI system.

DeFRCN-MAM: DeFRCN and multi-scale attention mechanism-based industrial defect detection method

ABSTRACT With the technology development, industrial defect detection based on deep learning has attracted extensive attention in the academic community. Different from general visual objects, industrial defects have the characteristics of small sample, weak visibility and irregular shape, which hinder the application of related studies. According to these problems, a few-shot object detection (FSOD) method based on Decoupled Faster R-CNN (DeFRCN) is proposed in this paper. Firstly, it includes fine-tuning processing, because of the small sample characteristics. To adapt to the invisible characteristics of defects, we introduce the Feature Pyramid Network (FPN) and Residual Attention Module (RAM) into DeFRCN, which can enhance the capture ability of multi-scale features and feature association information. Furthermore, the feature representation ability is strengthened by parallel connecting of two channels, consisting of R-CNN head, box classifier and box regression models. Finally, it is completed that the pre-training, fine-tuning and testing of the proposed network, with DAGM 2007 and NEU-DET public industrial defect datasets as the base class and flange shaft defect data collected in the laboratory as the new class. To verify the effectiveness of the proposed one, we compare them with other classical FSOD methods. The superiority of the proposed method is obvious.

Enhanced osteogenesis and physicochemical properties of PMMA bone cement with SrBG/HA porous core-shell microspheres

Artificial bone graft with osteoconductivity, angiogenesis, and immunomodulation is promising clinical therapeutics for the reluctant healing process of bone defects. Among various osteogenic substitutes, polymethyl methacrylate (PMMA) bone cement is a quit competitive platform due to its easy deployment to the bone defects with irregular shape and biomimetic mechanical properties. However, the biologically inert essence of PMMA is reliant on the passive osseointegration and cannot provide sufficient biologic cues to induce fast bone repair. Bioactive glass could serve as an efficient platform for the active osteogenesis of PMMA via ionic therapy and construction of alkaline microenvironment. However, the direct of deployment of bioactive glass into PMMA may trigger additional cytotoxicity and hinder cell growth on its surface. Hence we incorporated ionic therapy as osteogenic cue into the PMMA to enhance the biomedical properties. Specifically, we synthesized core-shell microspheres with a strontium-doped bioactive glass (SrBG) core and hydroxyapatite (HA) shell, and then composited them with PMMA to introduce multifunctional effects of HA incorporation, alkaline microenvironment construction, and functional ion release by adding microsphere. We prepared xSrBG@HA/PMMA cements (x = 30, 40, 50) with varied microsphere content and evaluated impacts on mechanical/handling properties, ion release, and investigated the impacts of different composite cements on proliferation, osteogenic differentiation, angiogenic potential, and macrophage polarization. These findings provide new perspectives and methodologies for developing advanced bone biomaterials to promote tissue regeneration.

Visible-Light Cross-Linkable Multifunctional Hydrogels Loaded with Exosomes Facilitate Full-Thickness Skin Defect Wound Healing through Participating in the Entire Healing Process.

The management of severe full-thickness skin defect wounds remains a challenge due to their irregular shape, uncontrollable bleeding, high risk of infection, and prolonged healing period. Herein, an all-in-one OD/GM/QCS@Exo hydrogel was prepared with catechol-modified oxidized hyaluronic acid (OD), methylacrylylated gelatin (GM), and quaternized chitosan (QCS) and loaded with adipose mesenchymal stem cell-derived exosomes (Exos). Cross-linking of the hydrogel was achieved using visible light instead of ultraviolet light irradiation, providing injectability and good biocompatibility. Notably, the incorporation of catechol groups and multicross-linked networks in the hydrogels conferred strong adhesion properties and mechanical strength against external forces such as tensile and compressive stress. Furthermore, our hydrogel exhibited antibacterial, anti-inflammatory, and antioxidant properties along with wound-healing promotion effects. Our results demonstrated that the hydrogel-mediated release of Exos significantly promotes cellular proliferation, migration, and angiogenesis, thereby accelerating skin structure reconstruction and functional recovery during the wound-healing process. Overall, the all-in-one OD/GM/QCS@Exo hydrogel provided a promising therapeutic strategy for the treatment of full-thickness skin defect wounds through actively participating in the entire process of wound healing.

Settling velocity of atmospheric particles in seawater: Based on hydrostatic sedimentation method using video imaging techniques

When atmospheric particles deposit to the ocean, their settling velocities and residence times associated are critical for their effects on oceanic ecosystems. We developed a hydrostatic sedimentation method using video imaging techniques to track particles of 5–20 μm in diameter falling into seawater and determine the particle settling velocities in relation to their diameter, shape, organic matter contained, and seawater salinity. The measured settling velocities varied from 0.025 to 0.41 mm/s. Irregular particle shape and organic matter contained in particles also, however, reduced the values. The settling velocities were decelerated by the dissolution process of particle in seawater. Combined with the experimental results, a formula for calculating the settling velocity formulae for atmospheric particles was estimated. Using this equation, the residence time of particles is estimated to be less than one month in continental shelf sea and more than 100 days in the oceans.

A Novel Multi-Scale Feature Map Fusion for Oil Spill Detection of SAR Remote Sensing

The efficient and timely identification of oil spill areas is crucial for ocean environmental protection. Synthetic aperture radar (SAR) is widely used in oil spill detection due to its all-weather monitoring capability. Meanwhile, existing deep learning-based oil spill detection methods mainly rely on the classical U-Net framework and have achieved impressive results. However, SAR images exhibit high noise, blurry boundaries, and irregular shapes of target areas, as well as speckles and shadows, which lead to the loss of performance in existing algorithms. In this paper, we propose a novel network architecture to achieve more precise segmentation of oil spill areas by reintroducing rich semantic contextual information before obtaining the final segmentation mask. Specifically, the proposed architecture can re-fuse feature maps from different levels at the decoder end. We design a multi-convolutional layer (MCL) module to extract basic feature information from SAR images, and a feature extraction module (FEM) module further extracts and fuses feature maps generated by the U-Net decoder at different levels. Through these operations, the network can learn rich global and local contextual information, enable sufficient interaction of feature information at different stages, enhance the model’s contextual awareness, and improve its ability to recognize complex textures and blurry boundaries, thereby enhancing the segmentation accuracy of SAR images. Compared to many U-Net based segmentation networks, our method shows promising results and achieves state-of-the-art performance on multiple evaluation metrics.

Photocatalytic activity of polyaniline‐potassium doped nickel oxide nanocomposites: A comprehensive investigation on multifaceted properties

AbstractPolyaniline‐potassium doped nickel oxide nanocomposites (PANI/KNO) were efficiently synthesized via an in situ chemical oxidation method, meticulously confirmed by comprehensive x‐ray diffraction, Fourier transformation infrared spectroscopy, energy dispersive x‐ray spectroscopy, selected area electron diffraction, and x‐ray photoelectron spectroscopy analyses. The crystallite size and strain exhibited distinct behaviors, while field emission scanning electron microscopy revealed a blend of irregular spherical shapes and nanofibers, further confirmed by high resolution transmission electron microscopy. These composites exhibited significantly enhanced thermal stability, surpassing PANI by 1.39 times, and showcased a band gap exceeding 3 eV, along with multiple photoluminescence peaks at room temperature (RT). Moreover, their RT AC conductivity experienced a remarkable surge by 4.91 times compared to pure polyaniline. Jonscher's Power Law model and the Havriliak–Negami relaxation model are employed to elucidate the electrical properties of composite materials. The composites displayed nonDebye relaxation behavior with increased conductivity, as observed through impedance analysis, electrical modulus evaluation, and dielectric constant assessment. Additionally, evident superparamagnetic properties and multiple magnetic domains further contributed to their effectiveness. The PANI/KNO composite demonstrated photocatalytic efficiency, achieving over 90% degradation of methylene blue dye across four cycles, thus highlighting its superior photostability. Furthermore, investigations into its response to pH variations and scavenger activity underscored its immense potential for highly effective photocatalytic applications.

Brain tumor segmentation in MRI with multi-modality spatial information enhancement and boundary shape correction

Brain tumor segmentation is currently of a priori guiding significance in medical research and clinical diagnosis. Brain tumor segmentation techniques can accurately partition different tumor areas on multi-modality images captured by magnetic resonance imaging (MRI). Due to the unpredictable pathological process of brain tumor generation and growth, brain tumor images often show irregular shapes and uneven internal gray levels. Existing neural network-based segmentation methods with an encoding/decoding structure can perform image segmentation to some extent. However, they ignore issues such as differences in multi-modality information, loss of spatial information, and under-utilization of boundary information, thereby limiting the further improvement of segmentation accuracy. This paper proposes a multimodal spatial information enhancement and boundary shape correction method consisting of a modality information extraction (MIE) module, a spatial information enhancement (SIE) module, and a boundary shape correction (BSC) module. The above three modules act on the input, backbone, and loss functions of deep convolutional networks (DCNN), respectively, and compose an end-to-end 3D brain tumor segmentation model. The three proposed modules can solve the low utilization rate of effective modality information, the insufficient spatial information acquisition ability, and the improper segmentation of key boundary positions can be solved. The proposed method was validated on BraTS2017, 2018, and 2019 datasets. Comparative experimental results confirmed the effectiveness and superiority of the proposed method over state-of-the-art segmentation methods.

Combined Effects of Banana Peels and Pith as Dual Natural Plant-Based Coagulants for Turbidity Removal

Combined Effects of Banana Peels and Pith as Dual Natural Plant-Based Coagulants for Turbidity Removal

Evaluation of the radial endosonographic image of peripheral lung lesions for choosing the bronchobiopsies in the differential diagnosis of tuberculosis, mycobacteriosis and malignancy

Objective. To evaluate endosonographic images (EGI) for different peripheral lung lesions (PPLs) genesis, to identify patterns of malignancy in the differential diagnosis of tuberculosis (TB), mycobacteriosis (NTM), malignancy neoplasm, to compare the main combinations of variants of the echo characteristics of lung lesions visualized during rEBUS navigation with the data of a comprehensive microbiological, cytological and morphological studies. Methods. A cohort retrospective study included 145 patients (81 women (mean age 44.94±0.24 years) and 64 men (mean age 47.91±0.24)), with newly diagnosed PPLs and with negative sputum TB tests, who underwent diagnostic navigational bronchoscopy (rEBUS) and surgical diagnostics, and EGI of PPLs was visualized with rEBUS. Depending on the final verification, were endosonographic images divided into 2 groups: the Neo group (n=63), whose biopsy (during bronchoscopic or surgical diagnosis) verified the malignancy PPls, and the TB+NTM group (n=82) – infectious PPLs caused by mycobacterial infection (73 cases – pulmonary TB, 9 cases – mycobacteriosis). Results. It was established that differences were statistically significant in shape, contour/border, and the presence of inclusions (linear arcs, zones of hypo-anechogenesis) between the comparison groups. If there are echoic features of malignancy PPLs on EGI with rEBUS (irregular shape (found in 60,3%), lobulated shape (in 17,4%), visualized but blurred contour (in 57.2%), fuzzy/poorly visualized contour (in 25.4%), heteroechoic structure, with “linear arcs” (49.2%) with an air bronchogram (in 36,5%)), tissue bronchobiopsy (brush and TBBL) are priority for cytological and histological examination. If there are echoic features of delimited infectious inflammation on EGI of PPLs in rEBUS (round or oval shape (found in 34.1% of cases), definite, closed contour (in 78.0%), homoechoic structure (29,3%), heteroechoic structure with zones of hypo-anechoicity (in 32.9%) with an air bronchogram (in 23.2%)), bronchobiopsy is a priority, allowing to cover the largest area of the lungs (BAL) for microbiological examination (microscopy, PCR-RT, culture) in combination with tissue biopsy (cytological examination). A classification has been created that includes 3 types of EGI to determine rational approaches to bronchobiopsy for the purpose of differential diagnosis of TB, mycobacteriosis and neoplasia. Conclusions. The use of EGI of PPLs assessment during bronchoscopy with rEBUS navigation determines rational approaches to the complex of navigational bronchobiopsies in the differential diagnosis of TB, mycobacteriosis and malignant processes.

ROS-scavenging bioactive scaffold orchestrates bone regeneration for osteoporotic bone defect repair

The repair of osteoporotic bone defects, particularly those with irregular shapes, remains a significant challenge due to insufficient bone regeneration and an abnormal level of reactive oxygen species (ROS), which impairs the balance of bone remodeling. Materials that intrinsically regulate ROS to rebalance the activation of osteoblasts and osteoclasts represent a highly desirable alternative to current grafting strategies for the management of osteoporotic defects. Inspired by the tea polyphenols and natural extracellular matrix of bone tissue, a novel biomimetic composite scaffold, the epichlorohydrin-crosslinked hydroxyethyl cellulose/soy protein isolate/polydopamine-coated hydroxyapatite composite sponge (EHSS/PHA) scaffold was designed to orchestrate bone-forming bone marrow mesenchymal stem cells (BMSCs) and bone-resorbing osteoclasts for the rejuvenation of osteoporotic bone defect repair. Benefiting from the distinctive capabilities of the EHSS/PHA, including scavenging active oxygen and osteoinduction, we found that the EHSS/PHA could inhibit the formation of osteoclasts and promote osteogenesis of BMSCs by up-regulating the ratio of Opg/Rankl and activating the β-catenin signaling pathway in an oxidative stress environment. With its antioxidant ability, osteogenic, and self-deploying capability, EHSS/PHA effectively promoted the repair of tibial defects in osteoporotic rats by removing excess reactive oxygen species at the defect site. Taken together, this work presents the EHSS/PHA with multifunctional properties that provide new insight into osteoporotic bone defects.

Enhancing Encapsulation Efficiency of Chavir Essential Oil via Enzymatic Hydrolysis and Ultrasonication of Whey Protein Concentrate-Maltodextrin.

This study focused on the characterization of emulsions and microparticles encapsulating Chavir essential oil (EO) by application of modified whey protein concentrate-maltodextrin (WPC-MD). Different physical, chemical, morphological, thermal, and antioxidant properties and release behavior of spray-dried microparticles were assessed. Antioxidant, solubility, emulsifying, and foaming activities of modified WPC were increased compared to those of primary material. The results indicated that the particle size distribution varied depending on the type of carriers used, with the smallest particles formed by hydrolyzed WPC (HWPC). Binary blends of modified WPC-MD led to improved particle sizes. The spray-drying yield ranged from 64.1% to 85.0%, with higher yields observed for blends of MD with sonicated WPC (UWPC). Microparticles prepared from primary WPC showed irregular and wrinkled surfaces with indentations and pores, indicating a less uniform morphology. The UWPC as a wall material led to microparticles with increased small cracks and holes on their surface. However, HWPC negatively affected the integrity of the microparticles, resulting in broken particles with irregular shapes and surface cracks, indicating poor microcapsule formation. Encapsulating EO using WPC-MD increased the thermal stability of EO significantly, enhancing the degradation temperature of EO by 2 to 2.5-fold. The application of primary WPC (alone or in combination with MD) as wall materials produced particles with the lowest antioxidant properties because the EO cannot migrate to the surface of the particles. Enzymatic hydrolysis of WPC negatively impacted microparticle integrity, potentially increasing EO release. These findings underscore the crucial role of wall materials in shaping the physical, morphological, thermal, antioxidant, and release properties of spray-dried microparticles, offering valuable insights for microencapsulation techniques.

Deep and wide search assisted evolutionary algorithm with reference vector guidance for many-objective optimization

Many-objective optimization problems appear in a large many practical cases, but maintaining the convergence and diversity of solutions becomes a big challenge. In response to this, a deep and wide search assisted evolutionary algorithm with reference vector guidance (RVEA-DWC) is proposed. In the algorithm, a novel environmental selection criterion based on substituting Iɛ+ indicator for distance is introduced to enhance the convergence, and once the selected individuals exceed the population size after multiple traversals, those with poor convergence or diversity are randomly eliminated. In addition, to tackle the irregular Pareto front shapes, the invalid reference vectors are deleted regularly, and a certain number of reference vectors with local diversity and global diversity are added, so as to conduct a overall search that combines deep search and wide search to balance exploitation and exploration. An experimental study of 5, 10, 15, 20 objectives is conducted on 60 test instances. The results demonstrate that the proposed algorithm is superior to the other twelve many-objective evolutionary algorithms.

Preparation of silymarin-loaded zein polysaccharide core–shell nanostructures and evaluation of their biological potentials

Abstract Silymarin-loaded zein polysaccharide core–shell nanoparticles (SZPCS-NPs) were synthesized where sodium alginate and pectin offer stability and controlled release qualities to zein, a maize protein, having excellent biocompatibility. The present study is an attempt to develop zein–silymarin polysaccharide core–shell nanostructures to enhance water solubility, thereby improving bioavailability and producing enhanced biological responses in living systems. SZPCS-NPs were prepared using pH-induced antisolvent precipitation method. Five different types of SZPCS-NPs were synthesized using different combinations of sodium alginate and pectin, namely P100–A00 (non-uniform size ranging from 20 to 100 nm), P70–A30 (spherical and uniform size measuring approximately 80 nm in diameter), P50–A50, P30–A70, and P00–A100 exhibited irregular shapes with the presence of some triangular and oval structures and non-uniform size ranging from 20 to 100 nm. The SZPCS-NPs P70–A30 possessed the best results in terms of shape, size, and other characterization studies. Furthermore, the SZPCS-NPs possessed a percent drug loading of 72.5% and entrapment efficiency of 51.7%, respectively. The resulting SZPCS-NPs exhibited an excellent relative bioavailability percentage of 97.4% in comparison to commercial silymarin, having 58.1%, and crude silymarin, having 46.97% bioavailability percentage, correspondingly. In addition, SZPCS-NPs possessed an almost two folds’ increase in antioxidant activity in comparison to crude and commercially available silymarin. Similarly, SZPCS-NPs also showed better stabilization in hepatic biomarker enzymes and possessed better hepatoprotective activity for a period of 6 weeks, in contrast to commercial and crude silymarin formulations.