Molecular docking analysis of stachydrine and sakuranetin with IL-6 and TNF-α in the context of inflammation

Specific blood metabolite associations with Gout: a Mendelian randomization study

OBJECTIVE

Gout, common metabolic disorders, have poorly understood links with blood metabolites. Exploring these relationships could enhance clinical prevention and treatment strategies.

METHODS

We applied bidirectional two-sample Mendelian randomization (MR) analysis, using data from a genome-wide association (GWAS) study of 486 blood metabolites. Gout data was obtained from FinnGen R8 (7461 gout and 221,323 control cases). We implemented the inverse variance-weighted (IVW) method for main analytical approach. Extensive heterogeneity, pleiotropy tests, leave-one-out analysis, and reverse MR were conducted to validate the robustness of our findings. Both Bonferroni and False Discovery Rate (FDR) corrections were used to adjust for multiple comparisons, ensuring stringent validation of our results.

RESULTS

Initial MR identified 31 candidate metabolites with potential genetic associations to gout. Following rigorous sensitivity analysis, 23 metabolites as potential statistical significance after final confirmation. These included metabolites enhancing gout risk such as X-11529 (OR = 1.225, 95% CI 1.112-1.350, P < 0.001), as well as others like piperine and stachydrine, which appeared to confer protective effects. The analysis was strengthened by reverse MR analysis. Additionally, an enrichment analysis was conducted, suggesting that 1-methylxanthine may be involved in the metabolic process of gout through the caffeine metabolism pathway.

CONCLUSION

Identifying causal metabolites offers new insights into the mechanisms influencing gout, suggesting pathways for future research and potential therapeutic targets.

Computational Analysis of a Marine-Derived Drug From Rhizophora mucronata Against the Capsid Protein of Rubella Virus

Background Rubella, commonly known as German measles, is caused by a single-stranded RNA genome. Vaccination is currently the most effective method for preventing rubella and its complications. Molecular docking, a computer-based technique used in drug discovery and development, is used to investigate the interactions between potential drug candidates and their target proteins. It predicts the binding interactions between small molecules (ligands) and the target protein. In this study, we examined a marine-derived drug from Rhizophora mucronata for its potential antiviral properties against the rubella capsid virus. Our objective was to identify the active inhibitory sites of the capsid virus. Materials and methods Protein and ligand molecules were retrieved from Protein Data Bank (PDB) and PubChem databases. The Lamarckian genetic algorithm was used to calculate molecular docking using Autodock Tools 1.5.7. The docking parameters used for each docked molecule were determined from 100 separate docking experiments with a maximum of 2.5×10-6 energy and a mutation rate of 2.0 and mass over ratio of 0.8. The results were recorded as docking parameter files (DPF). PyMOL was used to view and investigate the interactions between ligand fragments and rubella capsid protein. Results This approach plays a crucial role in the development of structure-based drugs. The results of the molecular docking suggest that Rhizophorin has the potential to bind with the rubella capsid protein. The strong binding affinity of -6.05 kcal/mol between the ligand and the protein further supports the potential of Rhizophorin as a therapeutic agent. The formation of hydrogen bonds between the ligand and amino acid residues Glu79, Arg82, and Thr118 indicates the significance of electrostatic interactions in the binding process. Furthermore, the hydrophobic interactions between the ligand and residues Ala81, Val84, Leu87, and Ile119 suggest the role of non-polar interactions in stabilizing the complex. The identified amino acid residues involved in these binding interactions could serve as potential targets for drug development. In future studies, experimental validation of the predicted interactions could provide further insights into the potential of Rhizophorin as an antiviral agent. Conclusion According to the findings of this study, the in silico investigation successfully identified a target for inhibiting the rubella virus (RuV) capsid receptor molecule. Future investigations on these compounds will require in vitro and in vivo studies using models that are more relevant to the medicinal potential of the capsid protein molecule.

Identification of binding partners that facilitate membrane-type 5 matrix metalloproteinase (MT5-MMP) processing of amyloid precursor protein

One of the pathological hallmarks of Alzheimer's disease (AD) is the presence of extracellular deposits of amyloid beta (Aβ) peptide. In addition to Aβ as the core component of the amyloid plaque, the amyloid precursor protein (APP) processing fragment Aβ was also found accumulated around the plaque. The APPη pathway, mainly mediated by membrane-type 5 matrix metalloproteinase (MT5-MMP), represents an important factor in AD pathogenesis. The proamyloidogenic features of MT5-MMP could result from interactions with APP when trafficking between organelles, so determination of the location within the cell of APPη cleavage and interacting proteins of MT5-MMP affecting this process will be of priority in understanding the role of MT5-MMP in AD. In the present study, MT5-MMP was found to be located in the nucleus, cytosol, and cytosolic subcellular granules of CHO cells that stably expressed wild-type human APP751. MT5-MMP fusion proteins were constructed that could localize enzyme production in the Golgi apparatus, endosome, ER, mitochondria, or plasma membrane. The fusion proteins significantly increased sAPPη when directed to the endosome, Golgi apparatus, plasma membrane, or mitochondria. Since the C-terminal region of MT5-MMP is responsible for its intracellular location and trafficking, this domain was used as the bait in a yeast two-hybrid screen to identify MT5-MMP protein partners in a human brain cDNA library. Identified binding partners included N4BP2L1, TMX3, EIG121, bridging Integrator 1 (BIN1), RUFY4, HTRA1, and TMEM199. The binding of N4BP2L1, EIG121, BIN1, or TMX3 to MT5-MMP resulted in the most significant increase in sAPPη production. Thus, the action of MT5-MMP on APP occurs in multiple locations within the cell and is facilitated by site-specific binding partners.

Emerging concepts of myosin 18A isoform mechanobiology in organismal and immune system physiology, development, and function

Alternative and combinatorial splicing of myosin 18A (MYO18A) gene transcripts results in expression of MYO18A protein isoforms and isoform variants with different membrane and subcellular localizations, and functional properties. MYO18A proteins are members of the myosin superfamily consisting of a myosin-like motor domain, an IQ motif, and a coiled-coil domain. MYO18A isoforms, however, lack the ability to hydrolyze ATP and do not perform ATP-dependent motor activity. MYO18A isoforms are distinguished by different amino- and carboxy-terminal extensions and domains. The domain organization and functions of MYO18Aα, MYO18Aβ, and MYO18Aγ have been studied experimentally. MYO18Aα and MYO18Aβ have a common carboxy-terminal extension but differ by the presence or absence of an amino-terminal KE repeat and PDZ domain, respectively. The amino- and carboxy-terminal extensions of MYO18Aγ contain unique proline and serine-rich domains. Computationally predicted MYO18Aε and MYO18Aδ isoforms contain the carboxy-terminal serine-rich extension but differ by the presence or absence of the amino-terminal KE/PDZ extension. Additional isoform variants within each category arise by alternative utilization or inclusion/exclusion of small exons. MYO18Aα variants are expressed in somatic cells and mature immune cells, whereas MYO18Aβ variants occur mainly in myeloid and natural killer cells. MYO18Aγ expression is selective to cardiac and skeletal muscle. In the present review perspective, we discuss current and emerging concepts of the functional specialization of MYO18A proteins in membrane and cytoskeletal dynamics, cellular communication and signaling, endocytic and exocytic organelle movement, viral infection, and as the SP-R210 receptor for surfactant protein A.

The Oncolytic Effect of Aerva lanata on Osteosarcoma Cell Lines via the Apoptotic Signaling Pathway

Introduction Osteosarcoma, a malignant bone tumor, poses significant treatment challenges, necessitating the development of alternative therapeutic strategies. Aerva lanata (A. lanata), a medicinal plant with traditional use in various healthcare systems, has anti-cancer properties. This study looks at the oncolytic effect of A. lanata extract on osteosarcoma cell lines (sarcoma osteogenic-Saos2). Aim The aim of this study was to investigate the oncolytic effect of Aerva lanata on Saos2 cell lines through the apoptotic signaling pathway. Materials and methods A. lanata extract was prepared using Soxhlet extraction, and its cytotoxic effects on Saos2 cells were assessed using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. Real-time polymerase chain reaction (RT-PCR) analysis of gene activity was used to assess the extract's effect on apoptotic signaling pathways. Results The MTT assay demonstrated a dose-dependent decrease in Saos2 proliferation following treatment with A. lanata extract at concentrations ranging from 50 μg to 200 μg. The standard deviations observed ranged from 1.414 to 7.071. Gene expression analysis revealed that the extract led to a reduction in the messenger ribonucleic acid (mRNA) levels of the anti-apoptotic marker B-cell lymphoma 2 (Bcl2), with standard deviations ranging from 1 to 0.535. Conversely, it induced an increase in the mRNA levels of the tumor suppressor protein p53, with standard deviations ranging from 1 to 1.835. These findings suggest that the extract modulates the apoptotic pathways of the Bcl2 and p53 genes.  Conclusion A. lanata extract exhibits promising anti-cancer activity against Saos2 osteosarcoma cell lines, inducing apoptosis by downregulating Bcl2 and increasing p53. The study's findings suggest that A. lanata may be useful as a natural treatment for osteosarcoma.

Molecular Docking of Seaweed-Derived Drug Fucoxanthin Against the Monkeypox Virus

Background The monkeypox virus (MPXV) is classified as a zoonotic virus of the Poxviridae family, resulting from the MPXV strain of the Orthopoxvirus genus. Seaweeds, or marine macroalgae, are abundant reservoirs of bioactive compounds that demonstrate diverse biological properties, such as antiviral actions. In the field of computational analysis, in silico analysis refers to the use of computer-based methods to study and assess biological systems and processes. To forecast the binding affinity and interaction between the discovered chemical and the target proteins of the MPXV, a molecular docking analysis was conducted. Aim The research aims to conduct an in silico examination of a protein-ligand interaction of a drug produced from seaweed that targets the MPXV.  Methodology Protein Data Bank (PDB) and PubChem databases provided MPXV methyltransferase and fucoxanthin ligand compounds. AutoDockTools 1.5.7 calculated the molecular docking using the Lamarckian genetic algorithm. Autogrid created a grid box around target 8B07 active site hotspot residues. Each docked molecule's docking parameters were obtained from 100 docking experiments with a maximum of 2.5 × 106 energy evaluations, a 0.02 mutation rate, and a 0.8 crossover rate. The population comprised 250 randomly selected volunteers. PyMOL was utilized to observe ligand fragment interactions. Results The binding energy of the ligand fucoxanthin was -5.46 kcal/mol. Fucoxanthin interacts with receptor molecules via hydrogen bonding at the amino acid level: Chain A: PHE188 and TYR189; and Chain B: LYS33, GLN37, GLY38, GLY96, ARG97, PHE115, PRO202, and SER203. The higher the negative docking score, the stronger the binding affinity between the receptor and ligand molecules, indicating that bioactive substances are more effective. Conclusion The findings of this study indicate that fucoxanthin, a pharmaceutical derivative generated from seaweed, had antiviral activity against the MPXV. This conclusion was reached based on protein-ligand interactions.

A primer on the use of machine learning to distil knowledge from data in biological psychiatry

Applications of machine learning in the biomedical sciences are growing rapidly. This growth has been spurred by diverse cross-institutional and interdisciplinary collaborations, public availability of large datasets, an increase in the accessibility of analytic routines, and the availability of powerful computing resources. With this increased access and exposure to machine learning comes a responsibility for education and a deeper understanding of its bases and bounds, borne equally by data scientists seeking to ply their analytic wares in medical research and by biomedical scientists seeking to harness such methods to glean knowledge from data. This article provides an accessible and critical review of machine learning for a biomedically informed audience, as well as its applications in psychiatry. The review covers definitions and expositions of commonly used machine learning methods, and historical trends of their use in psychiatry. We also provide a set of standards, namely Guidelines for REporting Machine Learning Investigations in Neuropsychiatry (GREMLIN), for designing and reporting studies that use machine learning as a primary data-analysis approach. Lastly, we propose the establishment of the Machine Learning in Psychiatry (MLPsych) Consortium, enumerate its objectives, and identify areas of opportunity for future applications of machine learning in biological psychiatry. This review serves as a cautiously optimistic primer on machine learning for those on the precipice as they prepare to dive into the field, either as methodological practitioners or well-informed consumers.

Computational Exploration of Single-Nucleotide Polymorphisms in the Human hRAS Gene: Implications and Insights

Background A group of genes called oncogenes includes the Harvey rat sarcoma virus (hRAS) gene. Along with hRAS, Kirsten rat sarcoma viral oncogene homolog (kRAS) and neuroblastoma RAS viral oncogene homolog (nRAS) genes belong to the Rat sarcoma (Ras) family of oncogenes. These three genes result in Rho guanosine triphosphate hydrolases (GTPases) as their protein product. Instructions for producing the protein hRAS, which is mainly involved in controlling cell division, are provided by the hRAS gene. The hRAS protein transfers signals from outside through a process called signal transduction. Because the hRAS protein is a GTPase, it changes the chemical guanosine-5'-triphosphate (GTP) into guanosine diphosphate (GDP). GTP and GDP molecules operate as switches to turn on and off the hRAS. This study aimed to anticipate the structure and stability of the protein resulting from missense single-nucleotide polymorphisms (SNPs) in the human hRAS genes. Methodology To investigate the possible negative effects associated with these SNPs, bioinformatic analysis is typically essential. The following tools were employed for forecasting harmful SNPs: Scale-Invariant Feature Transform (SIFT), Protein Analysis Through Evolutionary Relationships (PANTHER), non-synonymous SNP by Protein Variation Effect Analyzer (PROVEAN), and non-synonymous SNP by Single Nucleotide Polymorphism Annotation Platform (SNAP). Results The present study identified a total of 11 SNPs using the SIFT approach, which were shown to have functional significance. Only two of these 11 SNPs were determined to be tolerable, whereas nine were shown to be detrimental. Among the 11 SNPs analyzed, seven (Q61H, Q99H, K117R, A121D, A146V, R169W, R169Q) were classified as possibly damaging,and four (G13V, Q22K, Q61K, Q13V) were categorized as probably benign according to the predictions made by PANTHER tools. Therefore, the seven SNPs were identified as high-risk SNPs. Conclusions Given that SNPs have the potential to be candidates for cellular alterations brought on by mutations that are associated with cancer, this study provides vital information about how SNPs might be utilized as a diagnostic marker for cancer.

Chitosan Nanoparticles-Based Cancer Drug Delivery: Application and Challenges

Chitin is the second most abundant biopolymer consisting of N-acetylglucosamine units and is primarily derived from the shells of marine crustaceans and the cell walls of organisms (such as bacteria, fungi, and algae). Being a biopolymer, its materialistic properties, such as biodegradability, and biocompatibility, make it a suitable choice for biomedical applications. Similarly, its deacetylated derivative, chitosan, exhibits similar biocompatibility and biodegradability properties, making it a suitable support material for biomedical applications. Furthermore, it has intrinsic material properties such as antioxidant, antibacterial, and antitumor. Population studies have projected nearly 12 million cancer patients across the globe, where most will be suffering from solid tumors. One of the shortcomings of potent anticancer drugs is finding a suitable cellular delivery material or system. Therefore, identifying new drug carriers to achieve effective anticancer therapy is becoming essential. This paper focuses on the strategies implemented using chitin and chitosan biopolymers in drug delivery for cancer treatment.

Integrative analysis of multi-omics and imaging data with incorporation of biological information via structural Bayesian factor analysis

MOTIVATION

With the rapid development of modern technologies, massive data are available for the systematic study of Alzheimer's disease (AD). Though many existing AD studies mainly focus on single-modality omics data, multi-omics datasets can provide a more comprehensive understanding of AD. To bridge this gap, we proposed a novel structural Bayesian factor analysis framework (SBFA) to extract the information shared by multi-omics data through the aggregation of genotyping data, gene expression data, neuroimaging phenotypes and prior biological network knowledge. Our approach can extract common information shared by different modalities and encourage biologically related features to be selected, guiding future AD research in a biologically meaningful way.

METHOD

Our SBFA model decomposes the mean parameters of the data into a sparse factor loading matrix and a factor matrix, where the factor matrix represents the common information extracted from multi-omics and imaging data. Our framework is designed to incorporate prior biological network information. Our simulation study demonstrated that our proposed SBFA framework could achieve the best performance compared with the other state-of-the-art factor-analysis-based integrative analysis methods.

RESULTS

We apply our proposed SBFA model together with several state-of-the-art factor analysis models to extract the latent common information from genotyping, gene expression and brain imaging data simultaneously from the ADNI biobank database. The latent information is then used to predict the functional activities questionnaire score, an important measurement for diagnosis of AD quantifying subjects' abilities in daily life. Our SBFA model shows the best prediction performance compared with the other factor analysis models.

AVAILABILITY

Code are publicly available at https://github.com/JingxuanBao/SBFA.

CONTACT

qlong@upenn.edu.

Plausible Role of Stem Cell Types for Treating and Understanding the Pathophysiology of Depression

Major Depressive Disorder (MDD), colloquially known as depression, is a debilitating condition affecting an estimated 3.8% of the population globally, of which 5.0% are adults and 5.7% are above the age of 60. MDD is differentiated from common mood changes and short-lived emotional responses due to subtle alterations in gray and white matter, including the frontal lobe, hippocampus, temporal lobe, thalamus, striatum, and amygdala. It can be detrimental to a person’s overall health if it occurs with moderate or severe intensity. It can render a person suffering terribly to perform inadequately in their personal, professional, and social lives. Depression, at its peak, can lead to suicidal thoughts and ideation. Antidepressants manage clinical depression and function by modulating the serotonin, norepinephrine, and dopamine neurotransmitter levels in the brain. Patients with MDD positively respond to antidepressants, but 10–30% do not recuperate or have a partial response accompanied by poor life quality, suicidal ideation, self-injurious behavior, and an increased relapse rate. Recent research shows that mesenchymal stem cells and iPSCs may be responsible for lowering depression by producing more neurons with increased cortical connections. This narrative review discusses the plausible functions of various stem cell types in treating and understanding depression pathophysiology.

MiRNA Differences Related to Treatment-Resistant Schizophrenia

Schizophrenia (SZ) is a serious mental disorder that is typically treated with antipsychotic medication. Treatment-resistant schizophrenia (TRS) is the condition where symptoms remain after pharmacological intervention, resulting in long-lasting functional and social impairments. As the identification and treatment of a TRS patient requires previous failed treatments, early mechanisms of detection are needed in order to quicken the access to effective therapy, as well as improve treatment adherence. In this study, we aim to find a microRNA (miRNA) signature for TRS, as well as to shed some light on the molecular pathways potentially involved in this severe condition. To do this, we compared the blood miRNAs of schizophrenia patients that respond to medication and TRS patients, thus obtaining a 16-miRNA TRS profile. Then, we assessed the ability of this signature to separate responders and TRS patients using hierarchical clustering, observing that most of them are grouped correctly (~70% accuracy). We also conducted a network, pathway analysis, and bibliography search to spot molecular pathways potentially altered in TRS. We found that the response to stress seems to be a key factor in TRS and that proteins p53, SIRT1, MDM2, and TRIM28 could be the potential mediators of such responses. Finally, we suggest a molecular pathway potentially regulated by the miRNAs of the TRS profile.

An Evaluation of the Novel Biological Properties of Diterpenes Isolated from Plectranthus ornatus Codd. In Vitro and In Silico

Plectranthus ornatus Codd, the genus Plectranthus of the Lamiaceae family, has been used as traditional medicine in Africa, India and Australia. Pharmacological studies show the use of this plant to treat digestive problems. In turn, leaves were used for their antibiotic properties in some regions of Brazil to treat skin infections. The present study examines the anti-inflammatory, antioxidant and cytotoxic effects of the halimane and labdane diterpenes (11R*,13E)-11-acetoxyhalima-5,13-dien-15-oic acid (HAL) and 1α,6β-diacetoxy-8α,13R*-epoxy-14-labden-11-one (PLEC) and the forskolin-like 1:1 mixture of 1,6-di-O-acetylforskolin and 1,6-di-O-acetyl-9-deoxyforskolin (MRC) isolated from P. ornatus on lung (A549) and leukemia (CCRF-CEM) cancer cell lines, and on normal human retinal pigment epithelial (ARPE-19) cell line in vitro. Additionally, molecular docking and computational approaches were used. ADMET properties were analysed through SwissADME and proTox-II—Prediction. The results indicate that all tested compounds significantly reduced the viability of the cancer cells and demonstrated no cytotoxic effects against the non-neoplastic cell line. The apoptosis indicators showed increased ROS levels for both the tested A549 and CCRF-CEM cancer cell lines after treatment. Furthermore, computational studies found HAL to exhibit moderate antioxidant activity. In addition, selected compounds changed mitochondrial membrane potential (MMP), and increased DNA damage and mitochondrial copy number for the CCRF-CEM cancer cell line; they also demonstrated anti-inflammatory effects on the ARPE-19 normal cell line upon lipopolysaccharide (LPS) treatment, which was associated with the modulation of IL-6, IL-8, TNF-α and GM-CSF genes expression. Docking studies gave indication about the lowest binding energy for 1,6-di-O-acetylforskolin docked into IL-6, TNF-α and GM-CSF, and 1,6-di-O-acetyl-9-deoxyforskolin docked into IL-8. The ADMET studies showed drug-likeness properties for the studied compounds. Thus, halimane and labdane diterpenes isolated from P. ornatus appear to offer biological potential; however, further research is necessary to understand their interactions and beneficial properties.

Optimization of the extract from flower of Citrus aurantium L. var. amara Engl. and its inhibition of lipid accumulation

Flower of Citrus aurantium L. var. amara Engl. (CAVA) has been confirmed to have promising anti-obesity effects. However, the regulation of alkaloid extracts from flower of CAVA (Al) on lipid metabolism remain unknown. In this study, Al was optimized by ultrasound-assisted extraction using response surface methodology. The optimal conditions were ultrasonic time 72 min, ethanol concentration 78% and liquid/solid ratio 30 ml/g with the maximum alkaloid yield 5.66%. LC-MS assay indicated that the alkaloid compounds were enriched in Al after optimization. Nine alkaloid compounds were identified in Al by LC-MS assay and stachydrine, caffeine and cathine appeared as the major alkaloid compounds. Bioactivity assay showed that Al treatment significantly increased superoxide dismutase (SOD) activity, and reduced malonaldehyde (MDA) and reactive oxygen species (ROS) levels. Al administration also reversed oleic acid-induced hepatic steatosis in Hep G2 cells by inhibiting the expression of lipogenesis-signaling genes including fatty acid synthase (FAS), peroxisome proliferator-activated receptor subtype γ (PPARγ), uncoupling protein 2 (UCP2), and retinol binding protein (RBP4). However, OA-induced reduction of lipolysis-related gene carnitine palmitoyl transferase 1A (CPT1A) in Hep G2 cells was not improved by Al supplementation. Moreover, the increased SOD activity and decreased MDA and ROS contents were also observed in Caenorhabditis elegans by Al addition. Al intervention exhibited the ability to inhibit lipid accumulation in C. elegans by suppressing expression of lipid metabolism-related genes. These results suggested that the alkaloid extracts from the flower of CAVA showed great potential to regulate lipid metabolism. PRACTICAL APPLICATIONS: The extraction of alkaloid extracts from the flower of CAVA was optimized with a maximum yield of 5.66%. The regulatory effects and mechanisms of Al on lipid metabolism of Hep G2 cells and Caenorhabditis elegans were also investigated. More clinical studies are required to evaluate the potential of using alkaloids from the flower of CAVA as therapeutic agents against lipid metabolic disorders.

A review on omics-based biomarkers discovery for Alzheimer's disease from the bioinformatics perspectives: Statistical approach vs machine learning approach

Alzheimer's Disease (AD) is a neurodegenerative disease that affects cognition and is the most common cause of dementia in the elderly. As the number of elderly individuals increases globally, the incidence and prevalence of AD are expected to increase. At present, AD is diagnosed clinically, according to accepted criteria. The essential elements in the diagnosis of AD include a patients history, a physical examination and neuropsychological testing, in addition to appropriate investigations such as neuroimaging. The omics-based approach is an emerging field of study that may not only aid in the diagnosis of AD but also facilitate the exploration of factors that influence the development of the disease. Omics techniques, including genomics, transcriptomics, proteomics and metabolomics, may reveal the pathways that lead to neuronal death and identify biomolecular markers associated with AD. This will further facilitate an understanding of AD neuropathology. In this review, omics-based approaches that were implemented in studies on AD were assessed from a bioinformatics perspective. Current state-of-the-art statistical and machine learning approaches used in the single omics analysis of AD were compared based on correlations of variants, differential expression, functional analysis and network analysis. This was followed by a review of the approaches used in the integration and analysis of multi-omics of AD. The strengths and limitations of multi-omics analysis methods were explored and the issues and challenges associated with omics studies of AD were highlighted. Lastly, future studies in this area of research were justified.