Principle Component Analysis Research Articles

Exploring Bounded Component Analysis Using an ℓ∞ Norm Criterion

In this paper we propose a new criterion for the Blind Source Separation (BSS) of antisparse bounded sources, based on the sum of the ℓ∞-norm of the sources. Based on the observation that the mixing process of bounded sources with any mixing matrix with unitary Frobenius norm will increase the ℓ∞-norm of the sources, unless it is the identity matrix, the minimization of the sum of the ℓ∞-norm of the sources can be used for the estimation of a separation matrix. To that, a Principle Component Analysis technique followed by a Givens Rotations based optimization method can be used for the separation of independent bounded sources. Also, the Givens Rotations based optimization method can be used for the separation of correlated bounded sources mixed by a rotation matrix. We theoretically analyze the proposed criterion and assess its performance through numerical simulations involving three distinct types of bounded signals. Our theoretical and experimental findings underscore the efficacy of the ℓ∞ norm as a suitable contrast function for antisparse bounded sources, showcasing its superior performance relative to a state-of-the-art algorithm.

Research on Factors Influencing Housing Price based on Principle Component Analysis and Polynomial Regression

Housing price is influenced by natural, social, economic and other factors. Only by understanding and conducting specific research on these influencing factors can people grasp the laws of housing price movements. Focusing on this issue, this paper chooses the average unit housing prices and their influencing factors of 11 cities in Zhejiang Province in 2022 as the research objects, then establishes a model reflecting the quantitative relationship between housing price and influencing factors. Firstly, relevant data is collected from official websites, including housing prices and 10 main influencing factor indicators of above cities. Secondly, principal component analysis is used for data preprocessing, and five prominent indicators are selected by calculating the relative contribution rate of each factor. Then, by drawing functional images and solving correlation coefficients, a qualitative analysis is conducted on the basic algebraic structure of the regression model. The results indicate that all independent variables include independent terms and cross-terms. Subsequently, through polynomial regression, data matrices for both dependent variable and independent variables are set to solve the coefficients of constant term, linear terms, quadratic independent terms and quadratic cross-terms respectively. Therefore, a model of factors influencing housing price is established. Finally, residuals sum of squares, mean square error, regression sum of squares and determination coefficient are calculated in sequence for the regression model. It turns out that all the statistics are within the ideal range, indicating high precision of data fitting and verifying the accuracy and reliability of the model.

Seed Oil Contents, Fatty Acid Compositions, and Gossypol Concentrations of Some Okra Landraces

ABSTRACTOkra has recently attracted attention owing to its superior tolerance to high temperatures, greater adaptation to poor soil conditions, and having a robust plant structure. The plant contains a high amount of oil and valuable fatty acids; however, the main restriction of using okra seeds as an oil crop results from its gossypol contents. The aim of this study was to determine the oil content of okra landraces and to evaluate its potential as an oil crop. For this aim, seed oil content, fatty acid compositions of cold‐pressed seed oil, and gossypol concentrations of fruit, oil cake, and seed oil were investigated in a core collection of 26 okra landraces, lines, and cultivars. Individual plants were harvested at the full maturity stage, and seeds were harvested and dried under 35°C for 2 days prior to oil extraction. Oil content, fatty acid composition, and gossypol content were analyzed by NMR, GC‐FID, and HPLC, respectively. The calibration coefficients (r2) of all the methods were determined to be > 0.99. The seed oil content of the samples ranged between 12.15% and 18.83%. Linoleic (42.01%), palmitic (31.65%), oleic (18.39%), and stearic acids (3.20%) were found to be the largest fraction of the fatty acids. The data matrix from 19 fatty acids and oil content was subjected to Principle Component Analysis (PCA). As a result, 6 principal components (PCs, eigenvalues > 1) explained 83.84% of total variance in the data set, with PC1 contributing 32.69% of the total. Gossypol contents of the fruit, oil cake, and seed oil fractions ranged between LOQ‐2.12, < LOQ‐7.01, and < LOQ‐62.46 mg/kg, respectively. In conclusion, okra may have the potential to be an alternative oil crop for food/feed purposes due to the presence of reasonable oil content, high‐quality fatty acid variations, and very low amounts of toxic gossypols, warranting further breeding and agronomic studies.

Indoor moss biomonitoring proving construction-related pollution load from outdoors

Indoor pollution load in an office environment was investigated during ongoing construction works in the vicinity using the wet moss biomonitoring technique. Monitoring boxes were placed inside eight offices with mechanical ventilation located on three floors and with four different orientations to outdoor construction works for 8 weeks. Samples were collected every two weeks for two months, and the concentrations of selected elements: Al, As, Ca, Cd, Cr, Cu, Fe, Mg, Mn, Pb, Si and Zn were determined. Total deposition (composition, number and size of particles) was determined in the samples too. Correlation analysis complemented by Principle Component Analysis (PCA) was performed using a compositional approach. Concentrations correlated significantly among crustal elements and among road dust elements. PCA identified three groups of elements – representing construction dust, traffic origin and indoor origin, while those associated with construction dust had the highest contribution. The elevation (floor) affected the composition of the samples the most, the orientation to the pollution sources was secondary. Construction dust was most dominant on the 1st floor; samples from the sheltered sites were negatively associated with construction dust elements; here, the finest particles were also observed. Starting at 6 weeks of exposure, construction dust elements started to dominate in samples. Construction dust is a serious pollution problem, and its infiltration from the outdoors is apparent even in mechanical-ventilated indoor environments. Moss indoor monitoring technique was proven to be a suitable cost-effective alternative to automatic air samplers in determining the outdoor-originating pollution loads indoors.

Perceived Importance of Types and Characteristics of Support to Informal Caregivers among Spouse Caregivers of Persons with Dementia in Sweden: A Cross-Sectional Questionnaire-Based Study.

Informal caregivers play a crucial role in the care of individuals with dementia, and their caregiving may significantly impact their own health and well-being. This cross-sectional survey study focuses on the perceived importance of various types and characteristics of formal support in a convenience sample of caregivers aged 65 years or older (N = 175) caring for a spouse with dementia. Participants completed a questionnaire containing 17 items describing different types of support and 12 items describing different characteristics of support, rating their importance. The questionnaire also contained questions on various caregiving-related factors. Principle components analysis (PCA) was carried out on the importance ratings, separately, on the types of support items and the characteristics of support items. Each PCA produced three components. For types of support, they were Proficiency and Opportunity, Supportive Structures, Flexible Counselling. For characteristics of support, they included Respectful and Competent, Timely Support, and Accessible and Acceptable. The three characteristics of the support components all had higher mean importance ratings than the three types of support components. The content of some components indicated that while spouse caregivers rate support for their caregiving needs as important, they may not always differentiate their own needs from those of their partner with dementia. The negative impact of caregiving was the factor most strongly and consistently associated with the components' importance ratings. This study emphasizes the need for health and social care providers to address the unique needs of spouse caregivers while simultaneously ensuring the delivery of quality care for individuals with dementia.

Comprehensive in silico characterization of Arabidopsis thaliana RecQl helicases through structure prediction and molecular dynamics simulations

Helicases are ubiquitous enzymes with specific functions that contribute to almost all nucleic acid metabolic processes. The RecQ helicase family is essential for integrity in all organisms through DNA replication, repair, and recombination. This study investigated five RecQ-like helicases in Arabidopsis thaliana (AtRecQl) that exhibit diverse structural and physiochemical attributes and functions. Cis-regulatory element analysis identified stress, hormone, cell cycle, and development-responsive modules involved in various events in plant growth and development. Gene ontology analysis revealed that the five AtRecQl were associated with various cellular components, molecular functions, and biological processes. Protein-protein interaction analysis also implicated some in various abiotic stress processes. Structural analysis and molecular dynamics (MD) simulations were performed to examine conformational stability through root means square deviation and radius of gyration, showing stable AtRecQl protein structures. Free energy landscape analysis validated thermodynamically stable structures throughout the MD simulation. Principle component analysis and probability density functions from MD simulations provided satisfactory structural variational data for the complexes and limited coordinate movements. These insights might greatly benefit future studies.

Microstructural Characterization of Dry Powder Inhaler Formulations Using Orthogonal Analytical Techniques.

For locally-acting dry powder inhalers (DPIs), developing novel analytical tools that are able to evaluate the state of aggregation may provide a better understanding of the impact of material properties and processing parameters on the in vivo performance. This study explored the utility of the Morphologically-Directed Raman Spectroscopy (MDRS) and dissolution as orthogonal techniques to assess microstructural equivalence of the aerosolized dose of DPIs collected with an aerosol collection device. Commercial DPIs containing different strengths of Fluticasone Propionate (FP) and Salmeterol Xinafoate (SX) as monotherapy and combination products were sourced from different regions. These inhalers were compared with aerodynamic particle size distribution (APSD), dissolution, and MDRS studies. APSD testing alone might not be able to explain differences reported elsewhere in in vivo studies of commercial FP/SX drug products with different Advair® strengths and/or batches. Dissolution studies demonstrated different dissolution rates between Seretide™ 100/50 and Advair® 100/50, whereas Flixotide™ 100 and Flovent® 100 had similar dissolution rates between each other. These differences in dissolution profiles were supported by MDRS results: the dissolution rate is increased if the fraction of FP associated with high soluble components is increased. Principle component analysis was used to identify the agglomerate classes that better discriminate different products. MDRS and dissolution studies of the aerosolized dose of DPIs were successfully used as orthogonal techniques. This study highlights the importance of further assessing in vitro tools that are able to provide a bridge between material attributes or process parameters and in vivo performance.

Proposed methods of image recognition depend on the PCA

This paper suggest two method of recognition, these methods depend on the extraction of the feature of the principle component analysis when applied on the wavelet domain(multi-wavelet). First method, an idea of increasing the space of recognition, through calculating the eigenstructure of the diagonal sub-image details at five depths of wavelet transform is introduced. The effective eigen range selected here represent the base for image recognition. In second method, an idea of obtaining invariant wavelet space at all projections is presented. A new recursive from that represents invariant space of representing any image resolutions obtained from wavelet transform is adopted. In this way, all the major problems that effect the image and change its characteristic are solved through calculating invariant eigen range of the recursive resolution forms of all sub-images coefficient. These approaches employed here as multi-wavelet transform identifier with minimum Mahalanobis distance. All method recognition proposed in this paper are applied on different images. Different tables of image recognition resulted in accurate and fast.

The impact of a large airport on residents' quality of life and annoyance

A nearby large airport provides both positive and negative impact on surrounding communities. It can provide economic growth for a region near an airport, but also have a negative environmental and health-related impact with respect to aircraft induced noise exposure and air pollution. To study the impact of aircraft noise together with other aspects of air traffic in an airport region on the quality of life (QoL) and annoyance of residents in surrounding regions, a survey regarding nine QoL indicators was conducted for two Dutch communities. A comparison was made between one higher aircraft noise exposed community situated near Schiphol airport and one in Utrecht without a neighbouring airport and thus less exposed to aircraft noise. Results showed more aircraft noise-annoyed people living near the airport than people living further away. Using a Principle Component Analysis, the nine extracted factors supported previously theorized indicators defining QoL. Noticeable differences were found in neighborhood trouble, social contacts and aerospace perception between these two communities with the airport as main discriminator. Outcomes of this study help to develop a model to measure QoL and improve the understanding of the impact of noise exposure and other characteristics of a nearby airport on people's well-being.

Circulating MicroRNA as a Potential Biomarker for Skeletal Disease in Primary Hyperparathyroidism: A Case-control Study.

The goal of this study was to characterize the microRNA (miRNA) expression signatures in patients with Primary hyperparathyroidism (PHPT) and identify miRNA biomarkers of bone homeostasis. PHPT is associated with increased bone turnover and decreased bone mass. miRNA are markers of bone remodeling. We performed a prospective case-control study of postmenopausal females with PHPT and control subjects matched for race, age, and bone mineral density (BMD). We collected clinical and biochemical data, assessed BMD by dual-energy x-ray absorptiometry, and measured 27 serum miRNAs related to bone remodeling. We used linear regression to assess the correlation between miRNA levels, conventional biochemical markers, and BMD. A total of 135 subjects were evaluated, including 49 with PHPT (discovery group), 47 control patients without PHPT, and an independent validation cohort of 39 PHPT patients. Of 27 miRNAs evaluated, 9 (miR-335-5p, miR-130b-3p, miR-125b-5p, miR-23a-3p, miR-152-3p, miR-582-5p, miR-144-5p, miR-320a, and miR-19b-3p) were differentially expressed in PHPT compared with matched control subjects. All 9 differentially expressed miRNAs significantly correlated with levels of serum parathyroid hormone (PTH), and 8 of the 9 correlated with calcium levels. No differentially expressed miRNAs were consistently correlated with markers of BMD. Subjects with PHPT segregate from controls based on the signature of these 9 miRNAs on principle component analysis. These data suggest that PHPT is characterized by a unique miRNA signature that is distinct from postmenopausal and idiopathic osteoporosis. Levels of specific miRNAs significantly correlate with PTH, suggesting that bone remodeling in PHPT may be mediated in part by PTH-induced changes in miRNA.

Experimental and numerical investigation of the sensor effect on the acoustic emission during Pencil-lead Break tests on PMMA plates

Pencil leads are broken on PMMA plates at various source-sensor distances to evaluate the influence of the propagation distance and the sensor type on AE signals. The resulting waves are detected using a broadband or a resonant sensor. The ability of five transducers, PKBBI sensor, MICRO80 sensor, MICRO200HF sensor, Nano30 sensor, and WD sensor, to recreate the characteristic forms of plate waves is evaluated. Because of the response spanning between 300 to 400 kHz frequency range, Nano30 and MICRO80 sensors have a bigger magnitude in the S0 mode than in the A0 mode, especially for small sensor-source distances. Their different responses demonstrate why similar test specimens and test settings can provide diverse findings due to the sensor effect, which is significant for the use of AE data in the classification of AE sources. Then, based on the normalization method, we suggest a procedure to acquire equivalent values for the selected descriptors using Laplacian Score and Principle Component Analysis. In order to gain a thorough understanding of the sensor effect through 3D finite element simulation, a comparative analysis is conducted between the perfect contact sensor and the resonant sensor with sensor effect. The sensor effect arises from the convolution of the Fourier Transform of the signal with the sensitivity curve in the frequency domain. By comparing the waveforms in the time and frequency domain, it is useful to determine how the different sensors differ from the AE signals.

A Comprehensive Evaluation Method for Environmental Pollution in Tourist Attractions Based on Improved Principle Component Analysis

In order to address the problem of the reliability of current environmental pollution assessment methods, a comprehensive assessment method of environmental pollution in tourist scenic spots based on improved principle component analysis was proposed. First air, water, and soil pollution for data tourist attractions in the top 5 metropolitan cities of India are collected. Then, the original pollution data is cleaned, the data clustering method is used to classify the pollution data, and the comprehensive evaluation index system of environmental pollution in tourist attractions is established. Finally, the comprehensive evaluation method is implemented to calculate the evaluation values of various indicators, and the comprehensive assessment results of pollution at tourist attractions are obtained. The experimental results show that the correlation of the evaluation index is always controlled above 0.90, the convergence rate of the method is fast, the optimal solution can be approached within 150 iterations, and the application effect is good.

Advanced data-driven FBG sensor-based pavement monitoring system using multi-sensor data fusion and an unsupervised learning approach

Pavement health monitoring and assessment using sensor technology provides valuable insights into the condition of roads and helps in effective maintenance and management. This paper presents an advanced fiber Bragg grating (FBG) sensor-based pavement monitoring system that leverages multi-sensor data fusion techniques and an unsupervised learning approach for enhanced structural health assessment. The study commences with the fusion of data from various FBG sensors based on specific considerations and employing a novel function to improve the accuracy of pavement condition assessment. A dedicated data management system is introduced to address the challenges associated with continuous monitoring. This system automates data organization, pre-processing, and storage, facilitating historical trend analysis and post-processing. Then, the research introduces a novel unsupervised learning approach utilizing the density-based spatial clustering of applications with noise (DBSCAN) algorithm. This algorithm identifies abnormal patterns in the FBG sensor data, which may indicate structural anomalies or unexpected events, aiding the operator by automatically flagging abnormal data. By incorporating principle component analysis (PCA)-based feature fusion, the system reduces the dimensionality of the data, which enhances the efficiency of the clustering process. Various anomalies were detected in the data collected for four months from the test track. The cause and relevance of these anomalies were assessed to facilitate informed decision-making. This approach enables the operator to achieve early detection of potential structural issues and distinguish abnormal damage data from other anomalous data, thereby contributing to the overall success of the pavement monitoring system. In conclusion, the integration of advanced FBG sensor technology with multi-sensor data fusion and unsupervised learning techniques results in a sophisticated and robust pavement monitoring system. The presented approach enhances accuracy by detecting anomalies at any strain level, computational time using multi-sensor data fusion, and reliability of structural health assessment through anomaly evaluation. It also provides a framework for proactive decision-making, ultimately improving the longevity and structural integrity of critical infrastructure.

Authentication of porcine, bovine, and fish gelatins based on quantitative profile of amino acid and chemometric analysis

Gelatin is a biological macromolecule derived from the partial hydrolysis of collagen protein. This paper describes a sensitive and rapid method for the detection of gelatin sources based on the composition of amino acids. Hydrophilic interaction liquid chromatography coupled with tandem mass spectrometry (HILIC-MS/MS) and chemometric tools such as principle component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were used for the analysis and spectral classification, respectively. Twenty amino acids are identified and quantified with the limit-of-detection (LOD) of 0.03–2.62 μg/mL and limit-of-quantification (LOQ) of 0.10–7.93 μg/mL, a precision of 0.19–11.21% and 0.03–13.62% (%RSD) for intra and inter-day, respectively, and a total recovery of 85.1–107.6%. Porcine gelatin showed correlation with glycine, proline, hydroxyproline, tyrosine glutamine, and glutamic acid; Bovine gelatin was correlated with lysine, leucine, and isoleucine histidine, phenylalanine, and alanine, and fish gelatin showed correlation with methionine, threonine, serine arginine and cysteine in PCA analysis. Verification of the developed method was confirmed by using different commercial and laboratory prepared gelatin products containing gelatin and the samples were successfully categorized into their respective sources.

Anti-proliferative 2,3-dihydro-1,3,4-thiadiazoles targeting VEGFR-2: Design, synthesis, in vitro, and in silico studies

In this study, we present the design, synthesis, and evaluation of six new thiadiazole derivatives designed as VEGFR-2 inhibitors. The most promising compound, 18b, demonstrated promising inhibitory activity against VEGFR-2, with an IC50 value of 0.165 µg/mL. The in vitro assessments on MCF-7 and HepG2 cell lines revealed the superior anti-proliferative effects of compound 18b, exhibiting IC50 values of 0.06 and 0.17 µM, respectively. Further investigations into the cell cycle distribution of compound 18b on MCF-7 cells exhibited a cell cycle arrest at the S phase (52.96 %) and significantly reducing the percentage of cells in the G0-G1 and G2/M phases. Additionally, compound 18b demonstrated a remarkable pro-apoptotic effect, with 45.29 % total apoptosis, characterized by both early and late apoptosis, and minimal necrosis. These findings were corroborated by RT-PCR analysis, revealing a significant downregulation of the anti-apoptotic gene Bcl2 and upregulation of the pro-apoptotic gene BAX in compound 18b-treated cells compared to control MCF-7 cells. Moreover, in silico studies involving molecular docking, Density Functional Theory (DFT) calculations, Molecular Dynamics (MD) simulations, MM-GBSA, Principle Component Analysis of Trajectories (PCAT), in addition to Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) predictions underscored the molecular interactions, energetics, and pharmacokinetic properties of compound 18b and the other derivatives further supporting its potential. Our integrated approach, combining in vitro experimens with in silico predictions provides valuable insights into the therapeutic potential of compound 18b as a robust VEGFR-2 inhibitor and lays the groundwork for future optimization.

Investigating the factors affecting outward FDI of Vietnam: Does political index matter?

Considering the important role of foreign direct investment (FDI) in economic growth and integration, this paper seeks to thoroughly analyze the outward FDI pattern of Vietnam to its 15 major FDI recipients (accounting for approximately 94% of Vietnam's OFDI) using the gravity theory and a robust panel data approach for the comprehensive annual data over the period of 2007 – 2022. By determining the main destinations of Vietnam’s Outward FDI through the creation of social, economic, and political indices via the principle component analysis (PCA), the empirical results conclusively prove the positive impacts of the political index, social index, the existence of a common border, and accession to the WTO on the Vietnamese OFDI volume, while geographical distance is found to negatively impact the flow. As the main practical policy implications, issuing policies for sustainable economic growth, creating the novel strategy of FDI neighborhood policy, and fostering regionalism through FTAs (Free Trade agreements) are highly recommended.

Inhibitory action of antimicrobial peptides against the formation of carcinogenic and mutagenic heterocyclic amines in meat

Thermal processing of meat leads to the development of Maillard's reaction intermediates, and carcinogenic toxicants. For the first time, the effectiveness of three (HX-12A, HX-12B and HX-12C) antimicrobial peptides (AMPs) against the formation of heterocyclic amines (HAs) in chemical and meat model systems. The results showed that AMPs especially 12A and 12C have strong metal chelation potential (48 and 40% at 1 mg/ml) and antioxidant activity (35 and 25% at 1 mg/ml), respectively, which were endorsed by their secondary structure (FTIR analysis) in terms of high β-sheets (1628 cm−1 and 1672 cm−1) in those AMPs. UPLC-MS analysis revealed that 12A and 12C were the most capable AMPs in MeIQx and PhIP-producing chemical models, respectively, whereas 12B promoted the HAs formation even higher than control. In particular, 12C AMP significantly (P < 0.05) decreased the most abundant carcinogenic HAs (PhIP) up to 90% at 9 mg/100 g of fresh meat, whereas 12A inhibited up to 80% of mutagenic HAs at same level compared to control and 12B. Low Field Nuclear Magnetic Resonance (LF-NMR) test showed that inhibitory effect of 12A and 12C was mediated by means of retaining water (lower T22 and T23 relaxation time) inside the macromolecules. This favorable effect was also evidenced by significantly enhanced tryptophan fluorescent intensity. Finally, based on correlation and principle component analysis, the mechanism of action has been proposed. These outcomes recommend that 12A and 12C are potential AMPs for the attenuation of HAs in thermally processed meat-based products.

Financial Accountability, Resources and Competence of Proprietors, and Performance of Private Universities in Uganda

Abstract: The purpose of this study was to examine the effect of financial accountability on performance of private universities in Uganda. The study is guided by a hypothesis, that financial accountability have a statistically significant effect on the performance of private universities. The study taps into the voices of university stakeholders seeking their opinion on how to address the university performance challenges and 162 sample units were selected and participated in the study. The study follows the post-positivism philosophical stance which calls for the use of mixed techniques, while a descriptive, analytical and cross-sectional research design was utilized. Data was analyzed using two statistical software programs: Analysis of Moments of Structures (AMOS) for structural equation modeling (SEM), and SPSS was used for preliminary data analysis. In contrast, content analysis was employed in the qualitative approach. The results were presented using, Scatter plots, regression analysis, correlation coefficients and percentage tables while SEM was used to perform confirmatory factor analysis, principle component analysis, variance analysis, correlation coefficients and regression analysis. There was positive significant correlation between the financial accountability and the University performance (r = 0.641, P&lt;0.01). The results further indicate that financial accountability individually explains 41.1% (R Square Change = .411) of the variations in University performance. The study found out that all dimensions of financial accountability exhibit a positive and significant relationship with performance. Hence value for money, financial reporting and audited accounts can ensure transparent financial transactions avoid diversion of funds, create robust strategies for mobilizing resources and save funds that can be utilized to improve financial sustainability and education quality in private universities. The study recommends that private universities should establish transparent financial reporting, promote active engagement of proprietors in financial decision making and resource mobilization, adherence to fiscal responsibilities, and effective financial governance as well as training proprietors in university financial management.

Noise suppression in pulsed IR thermographic NDT: Efficiency of data processing algorithms

Various types of noise, which accompany active TNDT procedures using optical heating, have been analyzed, both numerically and experimentally. An emphasis has been made on the suppression of surface clutter, which represents local areas of varying absorptivity/emissivity. The concept of signal-to-noise that is typically used in defect detection has been applied to fixed pattern noise in order to compare capabilities of data processing algorithms in reducing surface clutter. The experimental investigation has been fulfilled on a special sample containing both subsurface air-filled defects and areas with varying emissivity/absorptivity. The best suppression of the fixed pattern noise was provided by the complex wavelet transform and principle component analysis. Because of 3D heat diffusion, clutter spot boundaries are often underlined by particular data processing algorithms thus producing specific contours. The test situations where subsurface defects are located under localized clutter spots have been analyzed to demonstrate an overshadowing effect of such spots when detecting hidden defects.

Computational Screening of FDA‐Approved Hepatitis C Drugs for Inhibition of VEGFR2 in Liver Cancer

AbstractLiver cancer (LC) is one of the most common tumours and the leading cause of cancer‐related death globally. Amidst the problems associated with existing treatments, such as hepatotoxicity, recurrence, drug resistance, and other adverse effects, researchers are under pressure to find alternatives. Towards a comprehensive rationalisation of the search for new anti‐LC drugs among approved ones, we employed an in‐silico approach to accelerate the selection of the most efficacious LC drugs. The FDA‐approved hepatitis C virus (HCV) drugs were docked with the LC protein using the AutoDock Vina software. Compared to the control compound, two FDA‐approved HCV drugs (DB09102 and DB09027) were selected based on their binding energies and interactions with the target protein, which showed comparable binding energies. Furthermore, these compounds were then subjected to molecular dynamic simulation, principle component analysis, and MMGBSA using the AMBER20 software, and the results showed stable complexes compared to the control complex. All things considered, this study will help the scientific community and society find a novel drug to treat LC.