Spherical Features Research Articles

An investigation of the internal morphology of asbestos ferruginous bodies: constraining their role in the onset of malignant mesothelioma

BackgroundAsbestos is a fibrous mineral that was widely used in the past. However, asbestos inhalation is associated with an aggressive type of cancer known as malignant mesothelioma (MM). After inhalation, an iron-rich coat forms around the asbestos fibres, together the coat and fibre are termed an “asbestos ferruginous body” (AFB). AFBs are the main features associated with asbestos-induced MM. Whilst several studies have investigated the external morphology of AFBs, none have characterised the internal morphology. Here, cross-sections of multiple AFBs from two smokers and two non-smokers are compared to investigate the effects of smoking on the onset and growth of AFBs. Morphological and chemical observations of AFBs were undertaken by transmission electron microscopy, energy dispersive x-ray spectroscopy and selected area diffraction.ResultsThe AFBs of all patients were composed of concentric layers of 2-line or 6-line ferrihydrite, with small spherical features being observed on the outside of the AFBs and within the cross-sections. The spherical components are of a similar size to Fe-rich inclusions found within macrophages from mice injected with asbestos fibres in a previous study. As such, the spherical components composing the AFBs may result from the deposition of Fe-rich inclusions during frustrated phagocytosis. The AFBs were also variable in terms of their Fe, P and Ca abundances, with some layers recording higher Fe concentrations (dense layers), whilst others lower Fe concentrations (porous layers). Furthermore, smokers were found to have smaller and overall denser AFBs than non-smokers.ConclusionsThe AFBs of smokers and non-smokers show differences in their morphology, indicating they grew in lung environments that experienced disparate conditions. Both the asbestos fibres of smokers and non-smokers were likely subjected to frustrated phagocytosis and accreted mucopolysaccharides, resulting in Fe accumulation and AFB formation. However, smokers’ AFBs experienced a more uniform Fe-supply within the lung environment compared to non-smokers, likely due to Fe complexation from cigarette smoke, yielding denser, smaller and more Fe-rich AFBs. Moreover, the lack of any non-ferrihydrite Fe phases in the AFBs may indicate that the ferritin shell was intact, and that ROS may not be the main driver for the onset of MM.

Comparison between Ethylcellulose and Polymethacrylate Topical Nanosponges Loaded with Butenafi ne Hydrochloride

In this study, nanosponges (NS) loaded with butenafi ne hydrochloride (a broad-spectrum benzylamine antifungal agent) were successfully prepared by emulsion solvent diff usion method using ethylcellulose or polymethacrylate as retarder polymers. PVA was used as a stabilizer. NS 18 formulations were formulated with various ratios of 1:1, 1:2, and 1:3 (%, w/w), using diff erent PVA concentrations of 0.25, 0.5, and 0.75 (%, w/w). The prepared formulations were tested for particle size in terms of diameter, which ranged from (73.6 ± 15.4–991 ± 11.2 nm) for ethylcellulose NS, and from (116 ± 0.96–1439.6 ± 237.4 nm) for polymethacrylate NS. The entrapment effi ciency of the selected formulas (F1, F10) were (74.12 ± 2.60%) and (72.89 ± 1.41%). The selected formulas F1 and F10 were also evaluated for their polydispersity, yield, and in-vitro drug release. The release of BFNS powder in F10 (90.42 ± 1.81%) was higher than that of ECNS F1 powder (78.53 ± 3.24%) within 24 hours. Evaluation tests of the scanning electron microscope (SEM), Fourier transform infrared ray (FTIR), and powder X-ray diff raction (PXRD) confi rmed the NS’ spherical and porous features, the absence of any drug polymer interaction, and the stability of the drug in the delivery system. The two formulas of F1 and F10 were found to exhibit prolonged drug release, which minimizes side eff ects and dosing frequency of BF drug application.

Selective determination of ellagic acid in aqueous solution using blue-green emissive copper nanoclusters

Development of beneficial sensors to analyze ellagic acid concentrations is of great importance for food safety and human health. Herein, a facile and fast fluorescent probe was carried out for the excellently selective and sensitive measurement of ellagic acid in real samples through histidine protected copper nanoclusters (histidine@Cu NCs) as a nanosensor. This as-developed histidine@Cu NCs were performed through UV–vis absorption spectroscopy, fluorescence spectroscopy, transmission electron microscopy, Fourier-transform infrared spectroscopy, X-ray photoelectron spectroscopy and fluorescence lifetime analysis. The TEM image revealed that this nanomaterial had spherical features with the average diameter of 2.5 ± 0.05 nm. The blue-green fluorescence of this Cu NCs was found under the UV light. Meanwhile, the maximum excitation and emission wavelength were located at 387 nm and 488 nm. After addition of ellagic acid, the fluorescence of histidine@Cu NCs was slowly weakened with excellent linear range of 0.5–300 μM and detection limit of 0.077 μM. The fluorescence weakening mechanism of this nanosensor were attributed to the inner filter effect (IFE) and static quenching. Finally, this as-established analysis platform was successfully employed to measure ellagic acid in real samples.

Ultra-lightweight ceramic scaffolds with simultaneous improvement of pore interconnectivity and mechanical strength

• Simultaneously improve ceramic scaffolds’ porosity/interconnectivity and strength. • Control oxidation and etching of Al particle to regulate hollow spherical feature. • Unique near elastic buckling results in unparalleled mechanical performance. • Coexistence of outstanding mechanical properties and in vitro bioactivity. The high porosity and interconnectivity of scaffolds are critical for nutrient transmission in bone tissue engineering but usually lead to poor mechanical properties. Herein, a novel method that combines acid etching (AE) with selective laser sintering (SLS) and reaction bonding (RB) of Al particles is proposed to realize highly improved porosity, interconnectivity, mechanical strength, and in vitro bioactivity in 3D Al 2 O 3 scaffolds. By controlling the oxidation and etching behaviors of Al particles, a tunable hollow spherical feature can be obtained, which brings about the distinction in compressive response and fracture path. The prevention of microcrack propagation on the in situ formed hollow spheres results in unique near elastic buckling rather than traditional brittle fracture, allowing an unparalleled compressive strength of 3.72 ± 0.17 MPa at a high porosity of 87.7% ± 0.4% and pore interconnectivity of 94.7% ± 0.4%. Furthermore, scaffolds with an optimized pore structure and superhydrophilic surface show excellent cell proliferation and adhesion properties. Our findings offer a promising strategy for the coexistence of outstanding mechanical and biological properties, with great potential for tissue engineering applications.

A Spatial Alignment Method for UAV LiDAR Strip Adjustment in Nonurban Scenes

LiDAR strip adjustment is a key prerequisite for subsequent applications based on point cloud data since it inevitably suffers from spatial discrepancies caused by laser ranging errors, mounting errors, etc. Most current LiDAR strip adjustment methods rely on the extraction of structural features which are often unsuitable for non-urban scenes. Alternative strip adjustment methods based on correspondence distance minimalization ignore spatial alignment. To overcome these limitations, this paper presents an accurate spatial alignment method for UAV LiDAR strip adjustment in non-urban scenes. Firstly, we construct a novel point cloud feature descriptor called Spherical Shell Point Feature (SSPF) to extract multi-dimensional non-structural features that are robust to non-urban point clouds. The constructed SSPF is then combined with point coordinates to generate embedded features, which simultaneously consider the point coordinates and spatial alignment. Finally, the embedded features are utilized by a two-stage matching method to match pair-wise points of two adjacent strips. The proposed method is validated on two non-urban datasets collected by two types of LiDARs, which reduces the digital surface model discrepancies by 0.252 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> and 0.221 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> , respectively, and proves its superiority compared to mainstream strip adjustment methods as well.

Surface-Compartmentalized Micelles by Stereocomplex-Driven Self-Assembly.

The unique corona structure of surface-compartmentalized micelles (Janus micelles, patchy micelles) opens highly relevant applications, e.g. as efficient particulate surfactants for emulsion stabilization or compatibilization of polymer blends. Here, stereocomplex-driven self-assembly (SCDSA) as a facile route to micelles with a semicrystalline stereocomplex (SC) core and a patch-like microphase separated corona, employing diblock copolymers with enantiomeric poly(L-lactide)/poly(D-lactide) blocks and highly incompatible corona-forming blocks (polystyrene (PS), poly(tert-butyl methacrylate)) is introduced. The spherical patchy SC micelles feature a narrow size distribution and show a compartmentalized, shamrock-like corona structure. Compared to SC micelles with a homogeneous PS corona the patchy micelles have a significantly higher interfacial activity attributable to the synergistic combination of an amphiphilic corona with the Pickering effect of nanoparticles. The patchy micelles are successfully employed in the stabilization of emulsions, underlining their application potential.

Label-free third harmonic generation imaging and quantification of lipid droplets in live filamentous fungi

We report the utilization of Third-Harmonic Generation microscopy for label-free live cell imaging of lipid droplets in the hypha of filamentous fungus Phycomyces blakesleeanus. THG microscopy images showed bright spherical features dispersed throughout the hypha cytoplasm in control conditions and a transient increase in the number of bright features after complete nitrogen starvation. Colocalization analysis of THG and lipid-counterstained images disclosed that the cytoplasmic particles were lipid droplets. Particle Size Analysis and Image Correlation Spectroscopy were used to quantify the number density and size of lipid droplets. The two analysis methods both revealed an increase from 16 × 10−3 to 23 × 10−3 lipid droplets/µm2 after nitrogen starvation and a decrease in the average size of the droplets (range: 0.5–0.8 µm diameter). In conclusion, THG imaging, followed by PSA and ICS, can be reliably used for filamentous fungi for the in vivo quantification of lipid droplets without the need for labeling and/or fixation. In addition, it has been demonstrated that ICS is suitable for THG microscopy.

Structure-activity strategy comparison of (NH4)2CO3 and NH4OH precipitants on MnOx catalyst for low-temperature NO abatement

Herein, the structure-activity strategy comparison of two different precipitants on MnOx catalyst for low-temperature NH3-SCR was investigated in detail. XRD results confirmed that ammonium carbonate (denoted as AC) could transfer manganese precursor from MnCO3 to Mn2O3 phase with temperature from 80 to 450 °C, while ammonium hydroxide (denoted as AH) facilitated the phase transition of MnOx from Mn3O4 to the final Mn5O8 phase. Further morphology defined that Mn-AC catalyst possessed a peculiar spherical feature while Mn-AH catalyst presented an accumulation of abundant nano-particles. The activity results suggested that Mn-AC catalyst possessed higher activity compared with Mn-AH catalyst at lower temperature, with over 60% conversion achieved from 100 to 225 °C and the maximum NO conversion reached circa (denoted as ca.) 85% at 175 °C. In addition, Mn-AC catalyst possessed stronger surface acidity and redox properties than Mn-AH catalyst, which served as an important motivation for the difference in activity. More manganese ions with higher valence and surface oxygen favored the superior redox circle of Mn-AC catalyst. Transient reaction revealed that the catalysts followed both Eley–Rideal (E-R) and Langmuir–Hinshelwood (L-H) pathways while the L-H pathway was harder to occur than the former due to the intense NOx transformation over catalysts. On account of these analyses, the possible structure-activity strategy comparison model of (NH4)2CO3 and NH4OH precipitants on MnOx catalyst was proposed.

Use of Spherical and Cartesian Features for Learning and Recognition of the Static Mexican Sign Language Alphabet

The automatic recognition of sign language is very important to allow for communication by hearing impaired people. The purpose of this study is to develop a method of recognizing the static Mexican Sign Language (MSL) alphabet. In contrast to other MSL recognition methods, which require a controlled background and permit changes only in 2D space, our method only requires indoor conditions and allows for variations in the 3D pose. We present an innovative method that can learn the shape of each of the 21 letters from examples. Before learning, each example in the training set is normalized in the 3D pose using principal component analysis. The input data are created with a 3D sensor. Our method generates three types of features to represent each shape. When applied to a dataset acquired in our laboratory, an accuracy of 100% was obtained. The features used by our method have a clear, intuitive geometric interpretation.

Spatial feature mapping for 6DoF object pose estimation

This work aims to estimate 6Dof (6D) object pose in background clutter. Considering the strong occlusion and background noise, we propose to utilize the spatial structure for better tackling this challenging task. Observing that the 3D mesh can be naturally abstracted by a graph, we build the graph using 3D points as vertices and mesh connections as edges. We construct the corresponding mapping from 2D image features to 3D points for filling the graph and fusion of the 2D and 3D features. Afterward, a Graph Convolutional Network (GCN) is applied to help the feature exchange among objects’ points in 3D space. To address the problem of rotation symmetry ambiguity for objects, a spherical convolution is utilized and the spherical features are combined with the convolutional features that are mapped to the graph. Predefined 3D keypoints are voted and the 6DoF pose is obtained via the fitting optimization. Two scenarios of inference, one with the depth information and the other without it are discussed. Tested on the datasets of YCB-Video and LINEMOD, the experiments demonstrate the effectiveness of our proposed method.

A Study on N-Spherical Histogram for Unbounded Tensor Features in Human Action Recognition

Recent years, Dense Trajectory features has a crucial role in extracting implicit features for action recognition. The method encloses motion and appearance descriptors to specify characteristics of each trajectory. Moreover, combining gradient and optical flow field using tensor product has made a strong positive impact on the result as we introduced in our previous work. In this paper, a breakthrough concept of encoding a high dimensional unbound space using spherical coordinate is introduced and imposed to obtain sophisticated spherical tensor features. The experimental result shows that our propose features outperforms other conventional ones and the combination of all feature channels achieves the highest accuracy rate in our selfrecorded dataset.

Spherical Convolutional Neural Networks for Survival Rate Prediction in Cancer Patients.

ObjectiveSurvival Rate Prediction (SRP) is a valuable tool to assist in the clinical diagnosis and treatment planning of lung cancer patients. In recent years, deep learning (DL) based methods have shown great potential in medical image processing in general and SRP in particular. This study proposes a fully-automated method for SRP from computed tomography (CT) images, which combines an automatic segmentation of the tumor and a DL-based method for extracting rotational-invariant features.MethodsIn the first stage, the tumor is segmented from the CT image of the lungs. Here, we use a deep-learning-based method that entails a variational autoencoder to provide more information to a U-Net segmentation model. Next, the 3D volumetric image of the tumor is projected onto 2D spherical maps. These spherical maps serve as inputs for a spherical convolutional neural network that approximates the log risk for a generalized Cox proportional hazard model.ResultsThe proposed method is compared with 17 baseline methods that combine different feature sets and prediction models using three publicly-available datasets: Lung1 (n=422), Lung3 (n=89), and H&N1 (n=136). We observed comparable C-index scores compared to the best-performing baseline methods in a 5-fold cross-validation on Lung1 (0.59 ± 0.03 vs. 0.62 ± 0.04). In comparison, it slightly outperforms all methods in inter-data set evaluation (0.64 vs. 0.63). The best-performing method from the first experiment reduced its performance to 0.61 and 0.62 for Lung3 and H&N1, respectively.DiscussionThe experiments suggest that the performance of spherical features is comparable with previous approaches, but they generalize better when applied to unseen datasets. That might imply that orientation-independent shape features are relevant for SRP. The performance of the proposed method was very similar, using manual and automatic segmentation methods. This makes the proposed model useful in cases where expert annotations are not available or difficult to obtain.

A SLAM System with Direct Velocity Estimation for Mechanical and Solid-State LiDARs

Simultaneous localization and mapping (SLAM) is essential for intelligent robots operating in unknown environments. However, existing algorithms are typically developed for specific types of solid-state LiDARs, leading to weak feature representation abilities for new sensors. Moreover, LiDAR-based SLAM methods are limited by distortions caused by LiDAR ego motion. To address the above issues, this paper presents a versatile and velocity-aware LiDAR-based odometry and mapping (VLOM) system. A spherical projection-based feature extraction module is utilized to process the raw point cloud generated by various LiDARs, hence avoiding the time-consuming adaptation of various irregular scan patterns. The extracted features are grouped into higher-level clusters to filter out smaller objects and reduce false matching during feature association. Furthermore, bundle adjustment is adopted to jointly estimate the poses and velocities for multiple scans, effectively improving the velocity estimation accuracy and compensating for point cloud distortions. Experiments on publicly available datasets demonstrate the superiority of VLOM over other state-of-the-art LiDAR-based SLAM systems in terms of accuracy and robustness. Additionally, the satisfactory performance of VLOM on RS-LiDAR-M1, a newly released solid-state LiDAR, shows its applicability to a wide range of LiDARs.

A Rigorous Feature Extraction Algorithm for Spherical Target Identification in Terrestrial Laser Scanning

Precise and rapid extraction of spherical target features from laser point clouds is critical for achieving high-precision registration of multiple point clouds. Existing methods often use linear models to represent spherical target characteristics, which have several drawbacks. This paper proposes a rigorous estimation algorithm for spherical target features based on least squares configurations, in which the point-cloud data error is used as a random parameter, while the spherical center coordinates and radius are used as nonrandom parameters, emphasizing correlation between spherical parameters. The implementation details of this algorithm are illustrated by deriving calculation formulas for three variance–covariance matrices: variance–covariance matrices of the new observations, variance–covariance matrices of the new observation noise, and variance–covariance matrices of random parameters and the new observation noise. Experiments show that the estimation accuracy of sphere centers using our method is improved by at least 5.7% compared to classical algorithms, such as least squares, total least squares, and robust weighted total least squares.

Clustering Matters: Sphere Feature for Fully Unsupervised Person Re-identification

In person re-identification (Re-ID) , the data annotation cost of supervised learning, is huge and it cannot adapt well to complex situations. Therefore, compared with supervised deep learning methods, unsupervised methods are more in line with actual needs. In unsupervised learning, a key to solving Re-ID is to find a standard that can effectively distinguish the difference (distance) between the features of images belonging to different pedestrian identities. However, there are some differences in the images captured by different cameras (such as brightness, angle, etc.). It is well known that the training of neural networks is mainly based on the distance between features, while in unsupervised learning, especially in unsupervised learning methods based on hierarchical clustering, the distance between features plays a more important role in the clustering phase. We improve the accuracy of a deep learning method based on hierarchical clustering under fully unsupervised conditions, starting from both feature and distance metrics. First, we propose to use spherical features, by normalizing the images in the feature space, to weaken the structural differences (length) between features, while saving the feature differences (direction) between different identities. Then, we use the sum of squared errors (SSE) as a regularization term to balance different cluster states. We evaluate our method on four large-scale Re-ID datasets, and experiments show that our method achieves better results than the state-of-the-art unsupervised methods.

Expandable Spherical Projection and Feature Concatenation Methods for Real-Time Road Object Detection Using Fisheye Image

Fisheye lens cameras are widely used in such applications where a large field of view (FOV) is necessary. A large FOV can provide an enhanced understanding of the surrounding environment and can be an effective solution for detecting the objects in automotive applications. However, this comes with the cost of strong radial distortions and irregular size of objects depending on the location in an image. Therefore, we propose a new fisheye image warping method called Expandable Spherical Projection to expand the center and boundary regions in which smaller objects are mostly located. The proposed method produces undistorted objects especially in the image boundary and a less unwanted background in the bounding boxes. Additionally, we propose three multi-scale feature concatenation methods and provide the analysis of the influence from the three concatenation methods in a real-time object detector. Multiple fisheye image datasets are employed to demonstrate the effectiveness of the proposed projection and feature concatenation methods. From the experimental results, we find that the proposed Expandable Spherical projection and the LCat feature concatenation yield the best AP performance, which is up to 4.7% improvement compared to the original fisheye image datasets and the baseline model.

Decoupled Multiple Speaker Direction-of-Arrival Estimator Under Reverberant Environments

Direction-of-arrival (DOA) estimation for multiple simultaneous speakers in reverberant environments is still one of the challenging tasks in the audio signal processing field. A recent approach addresses this problem using a spherical harmonics domain feature named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">relative harmonic coefficients</i> (RHC). Based on a bin-wise operation across the STFT (short-time Fourier transform) domain, this method detects the direct-path RHC in the first stage, followed by single source localization in the second stage. However, the method is computationally expensive as each STFT bin requires an exhaustive grid search over the two-dimensional (2-D) directional space. In this paper, we propose a significantly more computationally efficient alternative that decouples the azimuth and elevation 2-D search to two separate one-dimensional (1-D) search. The proposed multi-speaker localization algorithm comprises of two main steps, responsible for: (i) achieving a joint direct-path RHC detection and decoupled DOA estimation using 1-D search; and (ii) counting the number of speakers and estimating their DOAs based on the estimates from direct-path dominated STFT bins. Experiments using both simulated and real-life reverberant recordings confirm the significant computational complexity reduction while achieving competitive localization accuracy, compared to the baseline approaches. Although our proposed method performs in an unsupervised manner, it proves to be applicable even under unfavorable acoustic environments with a high reverberation level (e.g., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$T_{60}=1$</tex-math></inline-formula> second).

Learning to Adapt to Label-Scarce Image Domain via Angular Distance-Based Feature Alignment

Most recent domain adaptation (DA) methods deal with unsupervised setup, which requires numerous target images for training. However, constructing a large-scale image set of the target domain is occasionally much harder than preparing a smaller number of image and label pairs. To cope with the problem, a great attention is recently paid to supervised domain adaptation (SDA), which takes an extremely small amount of labeled target images for training (e.g., at most three examples per category). In the SDA setup, adapting deep networks towards target domain is very challenging due to the lack of target data, and we tackle this problem as follows. Given labeled images from source and target domains, we first extract deep features and project them to hyper-spherical space via l2-normalization. Afterwards, an additive angular margin loss is embedded so that deep features of both domains are compactly grouped on the basis of shared class prototypes. To further relieve domain discrepancy, a pairwise spherical feature alignment loss is incorporated. All of our loss functions are defined in the hyper-spherical space, and the advantage of each ingredient is analyzed in the literature. Comparative evaluation results demonstrate that the proposed approach is superior to existing SDA methods, achieving 60.7% (1-shot) and 64.4% (3-shot) average accuracies for the DomainNet benchmark dataset using the ResNet-34 backbone. In addition, by applying a semi-supervised learning scheme to a network initialized by our SDA method, we achieve the state-of-the-art performance on semi-supervised domain adaptation (SSDA) as well.

『說文』省聲⋅省形字의 재구조— 戰國 出土文獻의 몇 가지 예를 근거로

This study checks the classification contents of the generated organisms in “shuowen” through an advisor by referring to the classification of existing studies on the generated organisms, and analyzes the spherical cause. Abbreviated significand abbreviated phonetic is an additional analysis method other than the “six principles theory of Chinese script” system used as “從某, 從某省” of syssemantograph or “從某省, 某聲” of phonogram in the “shuowen” system. Existing researchers analyzed the attribution and assistant process of the creator or of “shuowen” according to their respective criteria. They also summarized examples of analysis based on ancient script form but as literature from the National Era was excavated, a new basis for estimating the process of shape change was prepared. Therefore, this study also believes that additional analysis of abbreviated significand abbreviated phoneticis necessary, and examines the graphic component relationship of abbreviated significand abbreviated phonetic through text analysis in the national era, and further examines the cause of formation of abbreviated significand abbreviated phoneticis. This study analyzed through national text using actual examples in Chapter 3. It can be seen that most of the letters that attempted to be analyzed as “abbreviated significand abbreviated phonetic” reflect phenomena such as “modified tone” and “apperception”, which are spherical features of the “Paleography stage”, and several meanings can be found below. First, when “abbreviated significand abbreviated phonetic” misrepresented the letter, it was found that the original meaning and interior of the letter were unknown mainly due to “corruption” and “consolidation” of the letter. Second, the letters interpreted as “abbreviated significand abbreviated phonetic” could be analyzed in different coined and spherical methods for each era. This was able to confirm the process of the at the ancent characters stage. Third, when the classification of unilateral among the abbreviated significand abbreviated phoneticis characters was incorrectly induced, the shape of the unilateral unit as well as the graphic unit mainly played a central role in form-meaning, reflecting the derivational method and the organization method.

Ligand Decorated Primaquine Loaded Nanocarriers for Liver Targeting for Triggered Anti-Malarial Activity.

The aim of the current research is to formulate a nano delivery system for effective delivery of primaquine for liver targeting to achieve the potential anti-malarial activity. Another objective of current development is to formulate a lactobionic acid conjugated polyphosphazene based nano delivery of primaquine for liver targeting to distinguish anti-malarial activity. The particle size, entrapment efficiency, in-vitro drug release pattern, hepatotoxicity, MTT assay, erythrocyte toxicity assay, histopathology study, HepG2 cell uptake study, anti-- malarial study, and organ-distribution was also carried out to estimate the activity and potential features of a nanoparticle system. The results obtained from the above analysis justify the efficiency and effectiveness of the system. The NMR studies confirm the conjugation pattern and the TEM represents the spherical morphological features of nanoparticles. The controlled release pattern from the in-vitro release study was observed and found to be 73.25% of drug release in 20 hrs and in the nano-size range (61.6± 1.56 nm) by particle size analysis.SGOT level, SGPT, ALP, and Parasitemia level of optimized drug-loaded PEGylated lactobionic acid conjugated polyphosphazene derivatized nanoparticles (FF) was found to lie in the safe range, showing that the formulation is non-toxic to the liver. Primaquine drug-loaded PEGylated lactobionic acid conjugated polyphosphazene polymeric nanoparticles showed higher cell uptake on HepG2 cell lines as compared to the drug-loaded in PEGylated polyphosphazene polymeric nanoparticles and plain drug.Percentage cell viability of drugloaded PEGylated lactobionic acid conjugated polyphosphazene derivatized nanoparticles was decreased by enhancing the concentration of prepared nanoparticle system accessed by MTT assay. From the studies, it can be concluded that the optimized formulation of drug-loaded PEGylated lactobionic acid conjugated polyphosphazene derivatized nanoparticles showed high liver targeting, least toxicity to the liver, controlled release of the drug, higher anti-malarial activity against hepatocytes at a low dose, more effectiveness, and can be treated as a potential candidate for anti-malarial therapy.