Search Results (374)

The ubiquity and environmental persistence of per- and polyfluoroalkyl substances (PFASs) have raised significant concerns about their detrimental effects on human health. Collective scientific efforts are increasingly focused on elucidating PFAS toxicity mechanisms and identifying potential low-impact PFAS structures that retain the exceptional properties of this chemical class. To advance the use of in silico methods in PFAS toxicity assessment, we developed a robust modelling framework for predicting PFAS acute oral toxicity class (high or low) in rats, leveraging the enhanced capabilities of the in-house Isalos Analytics Platform. The automated machine learning (autoML) functionality was employed to optimise four ML models—k-nearest neighbours (kNN), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and fully connected neural network (NN)—using Mold2 molecular descriptors, and to identify the top-performing model through five-fold cross-validation. The selected kNN model (k = 3) was used for predictions on the held-out testing set, achieving an accuracy of 81.5%, while a Shapley values analysis provided valuable insights into the factors influencing toxicity predictions. Furthermore, the nearest-neighbour-based methodology enabled a read-across structural analysis of PFAS similarity groups consisting of each testing set instance and its three closest neighbours in the training set. This analysis revealed a consistent association between polyaromatic and heterocyclic structural features and high acute oral toxicity. The developed, thoroughly validated read-across model is freely accessible through the INSIGHT RatTox web application as well as the INSIGHT Cheminformatics Platform in Enalos Cloud, supporting high-throughput screening of PFAS compounds and investigation of structural similarities with their nearest neighbours for enriched structural interpretation. Full article

Background: Radiomics has emerged as a promising approach to non-invasively characterize the molecular landscape of gliomas, providing quantitative, high-dimensional data derived from routine MRI. Given the recent shift toward molecularly driven classification, radiomics may support precision oncology by predicting key genomic, epigenetic, and phenotypic alterations without the need for invasive tissue sampling. This systematic review aimed to synthesize current radiomics applications for the non-invasive prediction of molecular biomarkers in gliomas, evaluating methodological trends, performance metrics, and translational readiness. Methods: This review followed the PRISMA 2020 guidelines. A systematic search was conducted in PubMed, Ovid MEDLINE, and Scopus on 10 January 2025, and updated on 1 February 2025, using predefined MeSH terms and keywords related to glioma, radiomics, machine learning, deep learning, and molecular biomarkers. Eligible studies included original research using MRI-based radiomics to predict molecular alterations in human gliomas, with reported performance metrics. Data extraction covered study design, cohort size, MRI sequences, segmentation approaches, feature extraction software, computational methods, biomarkers assessed, and diagnostic performance. Methodological quality was evaluated using the Radiomics Quality Score (RQS), Image Biomarker Standardization Initiative (IBSI) criteria, and Newcastle–Ottawa Scale (NOS). Due to heterogeneity, no meta-analysis was performed. Results: Of 744 screened records, 70 studies met the inclusion criteria. A total of 10,324 patients were included across all studies (mean 140 patients/study, range 23–628). The most frequently employed MRI sequences were T2-weighted (59 studies, 84.3%), contrast-enhanced T1WI (53 studies, 75.7%), T1WI (50 studies, 71.4%), and FLAIR (48 studies, 68.6%); diffusion-weighted imaging was used in only 7 studies (12.8%). Manual segmentation predominated (52 studies, 74.3%), whereas automated approaches were used in 13 studies (18.6%). Common feature extraction platforms included 3D Slicer (20 studies, 28.6%) and MATLAB-based tools (17 studies, 24.3%). Machine learning methods were applied in 47 studies (67.1%), with support vector machines used in 29 studies (41.4%); deep learning models were implemented in 27 studies (38.6%), primarily convolutional neural networks (20 studies, 28.6%). IDH mutation was the most frequently predicted biomarker (49 studies, 70%), followed by ATRX (27 studies, 38.6%), MGMT methylation (8 studies, 11,4%), and 1p/19q codeletion (7 studies, 10%). Reported AUC values ranged from 0.80 to 0.99 for IDH, approximately 0.71–0.953 for 1p/19q, 0.72–0.93 for MGMT, and 0.76–0.97 for ATRX, with deep learning or hybrid pipelines generally achieving the highest performance. RQS values highlighted substantial methodological variability, and IBSI adherence was inconsistent. NOS scores indicated high-quality methodology in a limited subset of studies. Conclusions: Radiomics demonstrates strong potential for the non-invasive prediction of key glioma molecular biomarkers, achieving high diagnostic performance across diverse computational approaches. However, widespread clinical translation remains hindered by heterogeneous imaging protocols, limited standardization, insufficient external validation, and variable methodological rigor. Full article

Background: Current histopathology- and molecular-based gold standards for diagnosing adult diffuse gliomas (ADGs) have inherent limitations in reproducibility and interobserver concordance, while being time-intensive and resource-demanding. Although hyperspectral imaging (HSI)-based computer-aided pathology shows potential for automated diagnosis, it often yields suboptimal accuracy due to the lack of complementary spatial and structural tumor information. This study introduces a multimodal fusion framework integrating HSI with routinely acquired preoperative magnetic resonance imaging (MRI) to enable automated, high-precision ADG diagnosis. Methods: We developed the Hyperspectral Attention Fusion Network (HAFNet), incorporating residual learning and channel attention to jointly capture HSI patterns and MRI-derived radiomic features. The dataset comprised 1931 HSI cubes (400–1000 nm, 300 spectral bands) from histopathological patches of six major World Health Organization (WHO)-defined glioma subtypes in 30 patients, together with their routinely acquired preoperative MRI sequences. Informative wavelengths were selected using mutual information. Radiomic features were extracted with the PyRadiomics package. Model performance was assessed via stratified 5-fold cross-validation, with accuracy and area under the curve (AUC) as primary endpoints. Results: The multimodal HAFNet achieved a macro-averaged AUC of 0.9886 and a classification accuracy of 98.66%, markedly outperforming the HSI-only baseline (AUC 0.9267, accuracy 87.25%; p < 0.001), highlighting the complementary value of MRI-derived radiomic features in enhancing discrimination beyond spectral information. Conclusions: Integrating HSI biochemical and microstructural insights with MRI radiomics of morphology and context, HAFNet provides a robust, reproducible, and efficient framework for accurately predicting 2021 WHO types and grades of ADGs, demonstrating the significant added value of multimodal integration for precise glioma diagnosis. Full article

Flaveria trinervia (Spreng) C. Mohr is a plant traditionally used in Mexican medicine. In this study, gas chromatography–mass spectrometry (GC–MS) combined with network pharmacology was employed to characterize volatile and semi-volatile metabolites from F. trinervia leaves and to explore their potential system-level mechanisms of action in inflammatory and tumor-related disorders. A dual extraction strategy (hexane/dichloromethane and acetone/chloroform) was applied, followed by GC–MS-based compound identification. Putative molecular targets were predicted using established pharmacological databases, and protein–protein interaction networks were constructed to identify topological features and enriched biological pathways. A total of 11 bioactive compounds were tentatively identified with an identity level of ≥80%, with seven shared between both extracts, including phytol, germacrene D, caryophyllene oxide, pinene isomers, squalene, and 2,2′:5′,2″-terthiophene, metabolites previously reported to exhibit antioxidant, anti-inflammatory, and cytotoxic activities. Network topology analysis identified ESR1, RXRA/B/G, NCOA2, and CYP19A1 as central nodes, reflecting convergence on signaling axes involved in apoptosis, cell proliferation, immune modulation, and transcriptional regulation pathways. Functional enrichment analysis revealed significant associations with KEGG pathways related to immune modulation, neuroendocrine regulation, and cancer-associated pathways. Collectively, these findings suggest a multitarget biological and multipathway pharmacological profile for F. trinervia, consistent with previously reported biological activities. The concordance between in silico predictions and existing experimental evidence strengthens the pharmacological relevance of the identified metabolites and supports their prioritization for further experimental validation, including mechanistic and pharmacokinetic studies, in metabolic, immune, neurological, and cancer-related contexts. Full article

This study aims to investigate the computation of the partition dimension of various anti-myocardial infarction drugs, a graph-theoretical invariant of molecular graphs representing these drugs, for understanding and computationally characterizing structural properties of molecular networks. To improve the computational modeling of this topological invariant, advanced neural network techniques, specifically graph neural networks (GNNs) and deep neural networks (DNNs), are adopted. The GNN captures topological and molecular connection features from the molecular graph structures, which are then input into the DNN model. The DNN further processes these features to estimate the partition dimension, evaluating training performance, performing regression analysis, and producing error histograms. The model’s predictions are validated against reference values. Moreover, by analyzing the role that symmetry plays in determining the calculation of partition dimension, studying how the GNN takes advantage of permutation invariance concept related to symmetry principles to provide the DNN with symmetry-invariant features, and relating the degree of molecular symmetry to the predictive model’s accuracy and performance, its structural interpretation rather than direct chemical behavior. This dual-model approach permits a comprehensive evaluation of the model’s effectiveness in apprehending the structural characteristics of molecular graphs derived from drug molecules. The results are explicated in detail, focused on prediction accuracy, error distributions, and regression results. Moreover, this graph-theoretical metric analysis of partition dimension supports structure-based drug analysis and computational modeling, rather than direct prediction of pharmacokinetic properties, by integrating artificial neural network applications into pharmaceutical research. Full article

Lennox–Gastaut syndrome (LGS) is a rare and severe developmental and epileptic encephalopathy characterized by multiple drug-resistant seizure types, mandatory tonic seizures, cognitive and behavioral impairment, and distinctive electroencephalographic features, including slow spike–wave discharges and generalized paroxysmal fast activity. Despite decades of therapeutic advances, LGS remains associated with profound lifelong disability and the absence of a single disease-defining molecular mechanism. Recent advances in genetics, neurophysiology, and network neuroscience have reframed LGS as a convergent network encephalopathy, in which diverse genetic, structural, metabolic, immune, and acquired insults funnel into shared molecular hubs, leading to thalamocortical network dysfunction. This framework helps explain the limited efficacy of purely syndrome-based treatments. This review synthesizes current evidence on electroclinical phenotyping, molecular and network pathogenesis, and contemporary diagnostic workflows and proposes a molecule-to-precision-therapy framework for LGS. We critically appraise pharmacologic, dietary, surgical, and neuromodulatory therapies, emphasizing drop seizures as a major driver of morbidity. Among available treatments, cannabidiol shows the most consistent and clinically meaningful efficacy for drop seizures, with benefits extending beyond seizure counts to seizure-free days and caregiver-relevant outcomes. Finally, we highlight key gaps and future directions, including etiology-stratified trials, network-guided interventions, and outcome measures that capture long-term developmental and quality-of-life impacts. Full article

Triple-negative breast cancer (TNBC) is an aggressive and clinically challenging subtype of breast cancer characterized by the absence of estrogen receptor, progesterone receptor, and HER2 expression. This molecular phenotype narrows the availability of targeted therapies and contributes to high rates of early relapse, therapeutic resistance, and poor clinical outcomes. Mounting evidence pinpoints the tumor microenvironment (TME) as a central driver of TNBC progression, immune evasion, and resistance to treatment. The TME encompasses a complex and dynamic network of immune and stromal cells, extracellular matrix components, and soluble mediators that collectively shape tumor behavior and influence therapeutic response. Notably, TNBC often displays an immunologically active microenvironment, marked by high levels of tumor-infiltrating lymphocytes and immune checkpoint expression, opening a window for immune-based therapeutic strategies. This narrative review summarizes current knowledge on the cellular, molecular, and structural features of the TNBC tumor microenvironment, with particular focus on immunosuppressive mechanisms mediated by tumor-associated macrophages, myeloid-derived suppressor cells, cancer-associated fibroblasts, and dysfunctional T cells. We describe the clinical development and therapeutic impact of monoclonal antibodies, including immune checkpoint inhibitors and antibody–drug conjugates. Additionally, we discuss strategies aimed at modulating the TME to enhance monoclonal antibody efficacy, including immune cell reprogramming, extracellular matrix remodeling, cytokine/chemokine blockade, and combination treatment strategies. Finally, we highlight the role of biomarker-driven patient stratification and personalized therapeutic strategies, addressing current challenges and future directions in TME-targeted drug development. Together, these insights underscore the potential of integrating immune modulation and monoclonal antibody-based therapies to improve outcomes for TNBC patients. Full article

Background: Rhodopseudomonas palustris is a metabolically versatile bacterium with significant biotechnological potential, including the ability to catabolize lignin and its heterogeneous breakdown products. Understanding the molecular determinants of growth on lignin-derived compounds is essential for advancing lignin valorization strategies under both aerobic and anaerobic conditions. Methods: R. palustris was cultivated on multiple lignin breakdown products (LBPs), including p-coumaryl alcohol, coniferyl alcohol, sinapyl alcohol, p-coumarate, sodium ferulate, and kraft lignin. Condition-specific transcriptomics and proteomics datasets were generated and used as input features to train machine-learning models, with experimentally measured growth rates as the prediction target. Artificial Neural Networks (ANNs), Random Forest (RF), and Support Vector Machine (SVM) models were evaluated and compared. Permutation feature importance analysis was applied to identify genes and proteins most influential for growth. Results: Among the tested models, ANNs achieved the highest predictive performance, with accuracies of 94% for transcriptomics-based models and 96% for proteomics-based models. Feature importance analysis identified the top twenty growth-associated genes and proteins for each omics layer. Integrating transcriptomic and proteomic results revealed eight key transport proteins that consistently influenced growth across LBP conditions. Re-training ANN models using only these eight transport proteins maintained high predictive accuracy, achieving 86% for proteomics and 76% for transcriptomics. Conclusions: This study demonstrates the effectiveness of ANN-based models for predicting growth-associated genes and proteins in R. palustris. The identification of a small set of key transport proteins provides mechanistic insight into lignin catabolism and highlights promising targets for metabolic engineering aimed at improving lignin utilization. Full article

Anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis is an immune-mediated neurological disorder driven by dysregulated neuroimmune interactions, yet the molecular architecture linking tumor-associated immune activation, peripheral immunity, and neuronal dysfunction remains insufficiently understood. In this study, we established an integrative computational framework that combines multi-tissue transcriptomic profiling, weighted gene co-expression network analysis, immune deconvolution, and machine learning-based feature prioritization to systematically characterize the regulatory landscape of the disease. Joint analysis of three independent GEO datasets spanning ovarian teratoma tissue and peripheral blood transcriptomes identified 2001 consistently dysregulated mRNAs, defining a shared tumor–immune–neural transcriptional axis. Across multiple feature selection algorithms, ACVR2B and MX1 were reproducibly prioritized as immune-associated candidate genes and were consistently downregulated in anti-NMDAR encephalitis samples, showing negative correlations with neutrophil infiltration. Reconstruction of an integrated mRNA-miRNA-lncRNA regulatory network further highlighted a putative core axis (ENSG00000262580–hsa-miR-22-3p–ACVR2B), proposed as a hypothesis-generating regulatory module linking non-coding RNA regulation to immune-neuronal signaling. Pathway and immune profiling analyses demonstrated convergence of canonical immune signaling pathways, including JAK-STAT and PI3K-Akt, with neuronal communication modules, accompanied by enhanced innate immune signatures. Although limited by reliance on public datasets and small sample size, these findings delineate a systems-level neuroimmune regulatory program in anti-NMDAR encephalitis and provide a scalable, network-based multi-omics framework for investigating immune-mediated neurological and autoimmune disorders and for guiding future experimental validation. Full article

Background/Objectives: Flavonoids are widely used as lead structures in drug discovery, and their pharmacological and metabolic properties are strongly influenced by structural features such as positional isomerism. This study aimed to compare the metabolic profiles and underlying mechanisms of two isoflavone-based positional isomers, ACF-02 (2-(4-hydroxy-3-methoxyphenyl)-6,7-dimethoxy-3-(4-methoxyphenyl)-4H-chromen-4-one) and ACF-03 (2-(3-hydroxy-4-methoxyphenyl)-6,7-dimethoxy-3-(4-methoxyphenyl)-4H-chromen-4-one). Methods: The metabolic pathways of synthetically prepared ACF-02 and ACF-03 were investigated using an in vitro incubation system with human liver microsomes (HLMs) supplemented with an NADPH-regenerating system, followed by liquid chromatography–high-resolution tandem mass spectrometry (LC–HRMS/MS) analysis. Metabolites were identified based on LC–HRMS/MS data and molecular networking-based node connectivity with the parent compounds. Major metabolites were further characterized by CYP phenotyping using recombinant CYP450 isoforms, and the potential for drug–drug interactions of ACF-03 was evaluated using a CYP probe substrate cocktail approach. Results: HLM incubation of ACF-02 and ACF-03 produced both hydroxylated and O-demethylated metabolites, with O-demethylation as the predominant pathway; notably, the most abundant O-demethylated metabolite differed in an isomer-dependent manner, occurring at the B₂ ring for ACF-02 and at the A ring for ACF-03, with distinct CYP isoform involvement. Molecular networking supported the relationships between the parent compounds and their metabolites, and both compounds exhibited relatively high metabolic stability with limited CYP inhibition. Conclusions: Despite differing only in the position of a single methyl substituent, ACF-02 and ACF-03 exhibited distinct isomer-dependent metabolic profiles. These findings demonstrate that even subtle positional isomerism can significantly influence metabolic behavior and should be carefully considered during lead optimization and drug design. Full article

Bone tissue engineering, as an important branch of regenerative medicine, integrates multidisciplinary knowledge from cell biology, materials science, and biomechanics, aiming to develop novel biomaterials and technologies for functional repair and regeneration of bone tissue. Hydrogels are among the most commonly used scaffold materials; however, conventional hydrogels exhibit significant limitations in physical properties such as strength, tensile strength, toughness, and fatigue resistance, which severely restrict their application in load-bearing bone defect repair. As a result, the development of high-strength hydrogels has become a research hotspot in the field of bone tissue engineering. This paper systematically reviews the latest research progress in this area: First, it delves into the physicochemical characteristics of high-strength hydrogels at the molecular level, focusing on core features such as their crosslinking network structure, dynamic bonding mechanisms, and energy dissipation principles. Next, it categorically summarizes novel high-strength hydrogel systems and different types of biomimetic hydrogels developed based on various reinforcement strategies. Furthermore, it provides a detailed evaluation of the application effects of these advanced materials in specific anatomical sites, including cranial reconstruction, femoral repair, alveolar bone regeneration, and articular cartilage repair. This review aims to provide systematic theoretical guidance and technical references for the basic research and clinical translation of high-strength hydrogels in bone tissue engineering, promoting the effective translation of this field from laboratory research to clinical application. Full article

Background: Advances in the understanding of molecular mechanisms of human diseases, along with the generation of large amounts of molecular datasets, have highlighted the variability between patients and the need to tailor therapies to individual characteristics. In particular, RNA-based therapies hold strong promise for new drug development, as they can be easily designed to target specific molecules. Gene and protein functions, however, operate within a highly interconnected network, and inhibiting a single function or repressing a single gene may lead to unexpected secondary effects. In this study, we focused on genes associated with Alzheimer’s disease, a progressive neurodegenerative disorder characterized by complex pathological processes leading to cognitive decline and dementia. Its hallmark features include the accumulation of extracellular amyloid-β plaques and intracellular neurofibrillary tangles composed of hyperphosphorylated tau. Methods: We built a protein interaction network subgraph seeded on five Alzheimer’s-associated genes, including tau and amyloid-β precursor, and integrated it with microRNAs in order to select regulated nodes, study the effects of their depletion on signaling pathways, and prioritize targets for microRNA-based therapeutic approaches. Results: We identified nine protein nodes as potential candidates (Pik3R1, Bace1, Traf6, Gsk3b, Akt1, Cdk2, Adam10, Mapk3 and Apoe) and performed in silico node depletion to simulate the effects of microRNA regulation. Conclusions: Despite intrinsic limitations of the approach, such as the incompleteness of the available information or possible false associations, the present work shows clear potential for drug design and target prioritization and underscores the need for reliable and comprehensive maps of interactions and pathways. Full article

Background: Systemic inflammation is an essential factor in the formation of the tumor microenvironment and has an impact on patient response to immune checkpoint inhibitors. Although there is a growing interest in biomarkers of inflammation, there is a gap in understanding their predictive value for response to nivolumab in clinical practice. The objective of this research was to design and assess a multi-algorithmic machine learning (ML) model based on regular systemic inflammation measurements to forecast the response of treatment to nivolumab. Methods: An analysis of a retrospective real-world cohort of 177 nivolumab-treated patients was performed. Baseline inflammatory biomarkers, such as neutrophils, lymphocytes, platelets, CRP, LDH, albumin, and derived indices (NLR, PLR, SII), were derived. After preprocessing, 5 ML models (Logistic Regression, Random Forest, Gradient Boosting, Support Vector Machine, and Neural Network) were trained and tested on a 70/30 stratified split. Accuracy, AUC, precision, recall, F1-score, and Brier score were used to evaluate predictive performance. The interpretability of the model was analyzed based on feature-importance ranking and SHAP. Results: Gradient Boosting performed best in terms of discriminative (AUC = 0.816), whereas Support Vector Machine performed best on overall predictive profile (accuracy = 0.833; F1 = 0.909; recall = 1.00; and Brier Score = 0.134) performance. CRP and LDH became the most common predictors of all models, and then neutrophils and platelets. SHAP analysis has verified that high CRP and LDH were strong predictors that forced the prediction to non-response, whereas higher lymphocyte levels were weak predictors that increased the response probability prediction. Conclusions: Machine learning models based on common inflammatory systemic markers give useful predictive information about nivolumab response. Their discriminative ability is moderate, but the high performance of SVM and Gradient Boosting pays attention to the opportunities of inflammation-based ML tools in making personalized decisions regarding immunotherapy. A combination of clinical, radiomic, and molecular biomarkers in the future can increase predictive capabilities and clinical use. Full article

Genome-based discovery provides a powerful approach for identifying bioactive natural products. In this study, Streptomyces sp. PSU-S4-23 was isolated from soil collected in southern Thailand. Genome analysis revealed a nonribosomal peptide synthetase (NRPS) biosynthetic gene cluster highly similar to the reference actinomycin D cluster, including canonical NRPS genes and a cytochrome P450 associated with oxidative tailoring. Genomic comparison indicated that this strain is distinct from its closest relative S. caeni CGMCC 4.7426^T with ANIb and dDDH values below the species delineation thresholds. In agar diffusion assays, the crude extract exhibited antibacterial activity against Staphylococcus aureus (MSSA and MRSA), Bacillus subtilis, Bacillus cereus, Enterococcus faecalis, Staphylococcus epidermidis, as well as inhibition of Pseudomonas aeruginosa and Acinetobacter baumannii. LC–MS/MS profiling of the crude ethyl-acetate extract was performed. GNPS feature-based molecular networking revealed ions corresponding to actinomycin X₂ (m/z 1269.6), D (m/z 1255.6), and I (m/z 1271.6), confirming production of multiple actinomycin analogs. These findings highlight Streptomyces sp. PSU-S4-23 as a promising actinomycin-producing strain with potential relevance to antibiotic discovery. Full article

Atherosclerosis (AS) is a leading cause of death and disability in type 2 diabetes mellitus (T2DM). However, the shared molecular mechanisms linking T2DM and atherosclerosis have not been fully elucidated. We analyzed AS- and T2DM-related gene expression profiles from the Gene Expression Omnibus (GEO) database to identify overlapping differentially expressed genes and co-expression signatures. Functional enrichment (Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG)) and protein–protein interaction (PPI) network analyses were then used to describe the pathways and interaction modules associated with these shared signatures, We next applied the cytoHubba algorithm together with several machine learning methods to prioritize hub genes and evaluate their diagnostic potential and combined CIBERSORT-based immune cell infiltration analysis with single-cell RNA sequencing data to examine cell types and the expression patterns of the shared genes in specific cell populations. We identified 72 shared feature genes. Functional enrichment analysis of these genes revealed significant enrichment of inflammatory- and metabolism-related pathways. Three genes—IL1B, MMP9, and P2RY13—emerged as shared hub genes and yielded robust ANN-based predictive performance across datasets. Immune deconvolution and single-cell analyses consistently indicated inflammatory amplification and an imbalance of macrophage polarization in both conditions. Biology mapped to the hubs suggests IL1B drives inflammatory signaling, MMP9 reflects extracellular-matrix remodeling, and P2RY13 implicates cholesterol transport. Collectively, these findings indicate that T2DM and AS converge on immune and inflammatory processes with macrophage dysregulation as a central axis; IL1B, MMP9, and P2RY13 represent potential biomarkers and therapeutic targets and may influence disease progression by regulating macrophage states, supporting translational application to diagnosis and treatment of T2DM-related atherosclerosis. These findings are preliminary. Further experimental and clinical studies are needed to confirm their validity, given the limitations of the present study. Full article