Search Results (2,085)

Cellular senescence represents a critical biological paradox in oncology. Although it evolved as a safety mechanism to halt tumorigenesis through stable cell cycle arrest, its persistence in tissues can alter the microenvironment, promoting tumor recurrence. In the context of glioblastoma (GBM), this phenomenon is critically important, as current standard therapies, such as radiotherapy and chemotherapy, inadvertently induce a state of senescence known as “therapy-induced senescence” (TIS). Senescent cells remain metabolically active and acquire a unique Senescence-Associated Secretory Phenotype (SASP), characterized by the release of pro-inflammatory cytokines, proteases, and growth factors. SASP reshapes the tumor microenvironment (TME) through paracrine signals, promoting immunosuppression, invasiveness, drug resistance and tumor recurrence. Different glial populations, including astrocytes, microglia, and oligodendrocyte precursor cells (OPCs), respond differently to senescence, specifically contributing to the creation of a permissive niche for tumor recurrence. To contrast the effects of this phenomenon, a promising therapeutic strategy has emerged, the “one-two punch,” which induces initial DNA damage followed by selective elimination of senescent cells with senolytic drugs. In this review, we analyze in detail the efficacy of targeted synthetic agents, such as the Bcl-2 family inhibitor Navitoclax, and natural bioactive compounds such as Quercetin and Fisetin. The analysis focuses on the molecular mechanisms through which these agents disrupt anti-apoptotic pathways (SCAPs) and inhibit the PI3K/AKT/mTOR axis, restoring sensitivity to apoptosis. We propose that the integration of senolytic adjuvants into standard clinical protocols may represent a crucial frontier for eliminating residual disease reservoirs and we also suggest the possibility of combining them with molecules with neuroprotective action to significantly improve the prognosis in GBM. Full article

The high morbidity and mortality of cancer pose a severe challenge to human health. Traditional diagnostic and therapeutic strategies still exhibit obvious limitations in early diagnostic sensitivity, therapeutic precision, and real-time monitoring of treatment efficacy. The development of nanotechnology has provided novel solutions for precision cancer theranostics. Among nanomaterials, gold nanoparticles (AuNPs) have become a research hotspot in tumor nanomedicine due to their tunable size and morphology, excellent localized surface plasmon resonance (LSPR) effect, and favorable biocompatibility. However, despite encouraging preclinical outcomes, several challenges hinder their clinical translation, including an incomplete understanding of long-term toxicity, complex in vivo biological interactions, the lack of standardized evaluation protocols, and regulatory uncertainties and manufacturing reproducibility issues. This paper systematically reviews the physicochemical and biological mechanisms of AuNPs in cancer theranostics, and summarizes the latest research advances of AuNPs in cancer detection and diagnosis (including biomarker detection and multimodal imaging) as well as in therapeutic fields, covering photothermal therapy (PTT), photodynamic therapy (PDT), radiosensitization, targeted drug and nucleic acid delivery, and immunotherapy-assisted strategies. Furthermore, we discuss the development of intelligent and stimuli-responsive theranostic nanoplatforms based on AuNPs, and outline their future prospects in precision medicine and personalized cancer therapy, with particular emphasis on the requirements for clinical translation, including safety evaluation, large-scale production, and regulatory approval pathways. Full article

Aquatic macrophytes constitute essential bioindicators of the ecological status of Mediterranean wetlands. We evaluated 136 Andalusian wetlands across four biogeographical regions (Sierra Morena, Betic Ranges, Guadalquivir Valley, and Coastal Zone) by contrasting two methodological approaches. We compared a standard biological valuation index, based on hydrophyte valuation and total species richness, with a biogeographical assessment focused strictly on the originality, singularity, and integrity of hydrophyte assemblages. Results revealed a critical nonlinear decoupling between both metrics. Traditional valuation prioritized the Coastal and Guadalquivir zones, inflating the value of communities saturated by widespread taxa and masking their lower structural integrity. Conversely, the biogeographical analysis identified Sierra Morena as the reservoir of highest structural stability despite its natural species poverty. Furthermore, residual analysis exposed highly original hidden jewels systematically undervalued by standard protocols. Since richness-dependent metrics risk neglecting unique hydrophyte components, we propose a dual conservation strategy integrating irreplaceability and structural integrity. Ultimately, this framework provides actionable insights for the sustainable management of Mediterranean aquatic biodiversity, aligning conservation practices with global ecological sustainability goals. We caution that management decisions based solely on richness thresholds may inadvertently prioritize common habitats over functionally unique but species-poor refugia. Full article

Class imbalance is a persistent challenge in supervised machine learning, particularly in biological datasets where minority classes represent functionally critical categories. Synthetic data generation has emerged as a principal strategy for mitigating this problem, yet systematic comparisons of classical and modern deep generative approaches remain limited. This study presents a comprehensive benchmark evaluation of four synthetic data generation methods—SMOTE, CTGAN, TVAE, and TabDDPM—across two well-established biological datasets from the UCI Machine Learning Repository: the E. coli protein localization dataset (307 samples, 6 features, 4 classes) and the yeast protein localization dataset (1299 samples, 8 features, 4 classes). Synthetic data quality was rigorously assessed using a multi-dimensional evaluation framework encompassing distributional fidelity (Fréchet Distance, Wasserstein Distance), machine learning utility (Train-on-Synthetic-Test-on-Real and Train-on-Real-Test-on-Real protocols using XGBoost version 3.2.0, Logistic Regression, Support Vector Machines, and Random Forest), and distinguishability (Classifier Two-Sample Test). The datasets are rather imbalanced. During the experiments, the dataset size increased to three times its original size while preserving the imbalanced class-sample ratio. To evaluate the quality of synthetic data, the max(AUC,1−AUC) score is proposed. This score is inversely proportional to classification performance, indicating that synthetic data are not easily distinguishable from real data. Per-class analysis reveals that minority classes remain the primary challenge across all generative methods. SMOTE and TabDDPM obtained the highest predictive utility F1-scores across both datasets. TVAE offers the strongest distributional fidelity among deep generative models, producing synthetic samples that are most difficult to distinguish from real data (lowest C2ST scores). CTGAN exhibits significant performance degradation on both small- and medium-scale datasets, with F1 utility ratios below 0.50. Full article

Lumbar spine disorders often require surgical intervention using medical implants to stabilize or replace damaged structures. As the prevalence of these surgeries increases due to an aging population, rigorous preclinical evaluation is critical. This narrative review aims to summarize current testing methods, identify gaps in clinical translatability, and explore the role of emerging computational technologies. Mechanical testing protocols established by the American Society for Testing and Materials (ASTM) and the International Organization for Standardization (ISO) provide essential standardized data on structural integrity but fail to replicate the complex biological interactions of the human spine. Similarly, animal models offer insights into biological responses like osseointegration but are limited by quadrupedal biomechanics and anatomical differences. Recent advancements in Artificial Intelligence (AI) and Finite Element Analysis (FEA) enable rapid, patient-specific modeling and high-throughput screening, significantly reducing the time and cost of physical testing. Future innovations include 3D-printed personalized implants, bio-responsive materials, and genetically modified animal models to bridge existing translatability gaps. In conclusion, improving the clinical success of lumbar spine implants requires an integrated framework that combines mechanical, biological, and computational approaches. This interdisciplinary collaboration is vital for developing safer and more effective treatments for patients. Full article

The SH-SY5Y cell line is widely used as an in vitro model for pharmacological and molecular investigations of Parkinson’s disease (PD). The use of SH-SY5Y cells in PD research critically relies on their ability to differentiate into a mature, post-mitotic, dopaminergic (DAergic) neuronal phenotype. However, SH-SY5Y cells are inherently heterogeneous since they are firstly catecholaminergic cells and may express diverse phenotypic markers besides the DAergic ones. These properties seem to be determined by the differentiation protocol that is employed, thus meaning it is crucial to obtain proper cell types. This systematic review aims to discuss the main differentiation protocols used in PD research over the last 30 years. They include inducers such as retinoic acid (RA), the phorbol ester TPA, and the BDNF. Among the 514 studies that were screened, 249 employed these inducers. Then, we quantitatively report the ability of these protocols to differentiate SH-SY5Y cells in mature DAergic neurons, evaluating morphology, differentiation markers, and DAergic markers among the studies that specifically compared differentiated to undifferentiated SH-SY5Y cells (61 studies over 249). As our research shows, despite the highest usage of the RA differentiation protocol, the combination of RA with the BDNF inducer seems to increase the expression and the acquisition of a DAergic phenotype. Nevertheless, during this analysis, some limitations emerged, highlighting the intrinsic phenotypic heterogeneity of these cells, thereby limiting their suitability according to the specific biological question under investigation. A deep investigation into the literature about the molecular phenotypic features of differentiated SH-SY5Y cells may eventually help us to understand the advantages and disadvantages of each protocol that was employed, and adequately set experiments around the PD research. Full article

Background/Objectives: Sex estimation from craniofacial morphology is a fundamental component of biological profile construction in forensic anthropology. Population-specific reference data for Thai individuals derived from computed tomography (CT) remain limited, and direct comparisons between discriminant function analysis (DFA) and machine learning classifiers are frequently complicated by inconsistent validation protocols. This study aimed to characterize sexual dimorphism in CT-derived craniofacial measurements, compare the classification performance of DFA, support vector machine (SVM), and random forest (RF) under a unified validation protocol, and demonstrate their practical application in a forensic context. Methods: CT images from 300 Thai adults (150 males, 150 females; age range 20–90 years) were obtained from Srinagarind Hospital, Khon Kaen University. Eight linear craniofacial measurements spanning the cranial vault, facial skeleton, nasal aperture, and orbital region were obtained from each case. DFA, SVM, and RF were developed and compared under a unified leave-one-out cross-validation protocol. Classification performance was assessed using accuracy, AUC, and Matthews correlation coefficient (MCC). Results: Seven of eight measurements exhibited statistically significant sexual dimorphism, with facial breadth and nasal height demonstrating the greatest dimorphism. DFA achieved the highest classification accuracy of 85.7%, AUC of 0.924, and MCC of 0.713, incorporating five measurements into the canonical function. SVM and RF achieved comparable accuracy of 84.7% and 84.0%, respectively. All three classifiers correctly classified both forensic application cases with high confidence. Conclusions: CT-derived craniofacial measurements provide a reliable basis for sex estimation in Thai adults. The convergence of performance across all three classifiers under a unified internal validation protocol strengthens confidence in the internally validated performance estimates. The derived discriminant function equation and saved machine learning models constitute a complementary and immediately applicable toolkit for CT-based forensic sex estimation in the Thai population. Full article

With the growing interest in personalized medicine, alternative biological matrices to blood are increasingly explored as sources of diagnostic information. Saliva has emerged as a promising diagnostic matrix due to its non-invasive collection, suitability for home sampling, and minimal requirements for personnel training. Numerous studies have demonstrated the presence of metabolites in saliva that enable disease diagnosis and monitoring. However, the influence of pre-analytical and analytical factors on salivary metabolomics outcomes remains insufficiently characterized. In this study, we investigated factors potentially affecting the number and abundance of detected metabolites in untargeted salivary metabolomics using liquid chromatography coupled with mass spectrometry (LC–MS). The impact of chromatographic column type, extraction protocol, and saliva type (stimulated versus resting) was evaluated. Additionally, the effect of swab type on analyte recovery was assessed. The use of a synthetic swab for saliva collection yielded results most comparable to those obtained without swabs, for both resting and stimulated saliva samples, indicating minimal pre-analytical interference. The greatest metabolite coverage was obtained using ACN:MeOH (1:1, v/v), with a ZIC-HILIC column for polar metabolites and a C18 column for non-polar metabolite separation. These findings demonstrate that swab type, chromatographic column, extraction solvent, and saliva type critically shape metabolite coverage in untargeted salivary metabolomics. Importantly, the distinct metabolic profiles of resting and stimulated saliva suggest that these matrices may provide complementary clinical insights, underscoring the need for saliva type selection tailored to specific diagnostic and biomarker discovery objectives. Full article

Transcriptome profiling is a cornerstone of functional genomics, enabling the detailed characterization of gene expression in health and disease. Bulk RNA sequencing (bulk RNAseq) remains the most widely used approach in clinical and large-cohort studies due to its cost-effectiveness, robustness, and comprehensive transcriptome coverage. However, bulk RNAseq inherently averages gene expression signals across heterogeneous cell populations, thereby masking cellular diversity and obscuring rare cell types. In contrast, single-cell RNA sequencing (scRNAseq) enables a high-resolution analysis of cellular heterogeneity, allowing the identification of distinct cell types, transitional states, and developmental trajectories. Nevertheless, scRNAseq is associated with higher cost, limited scalability, increased technical noise, sparse expression matrices, and protocol-dependent biases introduced during tissue dissociation or nuclear isolation. In this review, we summarize the conceptual and methodological foundations of integrating bulk RNAseq and scRNAseq data, emphasizing their complementary strengths and limitations. We discuss how scRNAseq-derived cell-type atlases can serve as reference matrices for computational reconstruction (deconvolution) of bulk RNAseq profiles and examine key sources of technical and biological variability. Furthermore, we outline major integration strategies, including reference-based deconvolution, pseudobulk aggregation, and Bayesian joint modeling to provide an overview of widely used analytical tools and essential components of scRNAseq data processing workflows. Full article

Introduction: Lung cancer remains the leading cause of cancer-related mortality among men in Poland. Prognosis is generally poor, largely due to late diagnosis at advanced stages and the aggressive biological nature of the disease. Aim: This study aimed to evaluate the effectiveness of various treatment modalities and determine their impact on overall survival in male patients diagnosed with small-cell (SCLC) and non-small-cell lung cancer (NSCLC). Methods: This retrospective cohort study analyzed 1431 men (mean age: 61.5 years) treated at the Katowice Oncology Center in Poland between 2002 and 2012. Overall survival was assessed using the Kaplan–Meier method and multivariable Cox proportional hazards regression. Evaluated prognostic factors included clinical stage, surgical intervention (partial or total lung resection), first-line treatment regimen, and the number of treatment cycles. Results: Survival probabilities declined progressively with advancing clinical stage for both SCLC and NSCLC. Patients who underwent surgical resection demonstrated significantly longer survival compared to non-surgically treated patients (p < 0.001). Furthermore, combined radiochemotherapy yielded superior therapeutic outcomes compared to chemotherapy alone. In the non-surgical NSCLC cohort, first-line treatment with platinum derivatives combined with gemcitabine resulted in the highest 1-year survival rate compared to other pharmacological schemes. Discussion: The high mortality observed within the first 12 months post diagnosis reflects the late-stage presentation common during the study period. The findings align with established oncological principles, confirming that surgical resection and multimodal therapies offer the greatest survival advantages for eligible patients. Conclusions: Survival rates for both SCLC and NSCLC are overwhelmingly dictated by early diagnosis and the feasibility of surgical resection. Improving long-term outcomes depends heavily on implementing effective lung cancer screening programs to detect the disease at operable stages and utilizing optimized combined treatment protocols. Full article

Background: The ability of synthetic plastics to persist in the environment and the accumulation of microplastics has intensified the need to explore biological mechanisms capable of interacting with, and possibly degrading, polymeric materials. Microbial enzymes that have extensive catalytic flexibility represent promising candidates in this context. Aim: This study set out to examine the molecular interaction patterns and dynamical stability of Pseudomonas aeruginosa elastase (LasB) with representative structural fragments of typical synthetic plastics to assess the suitability of the enzyme to polymer-derived substrates. Methods: The crystallographic structure of LasB (PDB ID: 1EZM) was retrieved from the Protein Data Bank and pre-prepared with the help of AutoDock4.2.6 Tools. Those polymer-derived ligands that were associated with the major industrial plastics such as polyamide (PA), polyvinyl chloride (PVC), polycarbonate (PC), poly-ethylene terephthalate (PET), polymethyl methacrylate (PMMA), and polyurethane (PUR) were retrieved in the PubChem database and geometrically optimized with the help of the MMFF94 force field. AutoDock Vina, with a specific grid box around the catalytic pocket, including Zn²⁺ ion, was used to perform molecular docking simulations. PyMOL and BIOVIA Discovery Studio software were used to analyze binding conformations, interaction residues and types of intermolecular contacts. Phosphoramidon, a known metalloprotease inhibitor, served as a positive control to confirm the docking protocol. Additional assessment of the structural stability and conformational behavior of the enzyme–ligand complexes was conducted by molecular dynamics (MD) simulations with the Desmond engine and explicit solvent model in a 50 ns trajectory using the OPLS4 force field. RMSD, RMSF, radius of gyration, hydrogen bonding analysis and solvent accessibility parameters were used to measure structural stability. Results: The docking experiment showed varying binding affinities with the test polymers. Polycarbonate (−5.774 kcal/mol) and polyurethane (−5.707 kcal/mol) had the highest in-teractions with the LasB catalytic pocket, polyamide (−5.277 kcal/mol) and PET (−4.483 kcal/mol) followed PMMA and PVC, which had weaker affinities. The following were the important residues involved in interaction networks: Glu141, His140, Val137, Arg198, Tyr114, and Trp115 that were implicated in interaction networks with hydrophobic interactions, π-cation interactions and van der Waals forces that were the major stabilization forces. MD simulations had stabilized complexes, and RMSD values were found to be within acceptable ranges of stability, and ligand-specific changes (around 1.0-3.2 A), which is also in line with stable protein-ligand systems. Phosphoramidon used as a positive control had an RMSD of 1.205 A which is within this stability range. PCA determined various ligand-bound conformational states of LasB with PA in com-pact state, PC and PVC in intermediate states and PUR, PMMA and PET in ex-panded conformations, indicating structur-al stability and adaptability of the binding pocket. Conclusion: These findings show that LasB has a structurally flexible catalytic pocket that can accommodate a wide range of polymer-derived ligands. These results offer an insight into the recognition of enzymes with polymers at the molecular level and also indicate that LasB might help in the interaction of microorganisms with synthetic plastics in environmental systems. Full article

Succinic acid (SA) is a naturally occurring dicarboxylic acid with diverse biological roles, including participation in cellular energy metabolism and signaling. Despite its wide industrial use, clinical and in vivo evidence supporting the application of SA in cosmetics, cosmetology, dermatology, and aesthetic medicine remains limited, although mechanistic and experimental studies describing its biological activity are increasingly available. This review summarizes the chemical properties, natural occurrence, and physiological functions of SA, with a focus on its potential in topical and intradermal applications. The use of SA in cosmetic formulations, including personal care products, moisturizers, and masks, is discussed, alongside its emerging roles in the management of acne vulgaris and rosacea, hyperpigmentation, and as a chemical exfoliant and biostimulatory agent. Available studies suggest that SA can modulate inflammation, enhance microcirculation, support fibroblast proliferation, and stimulate collagen and elastin production, although most clinical evidence derives from small-scale or preliminary studies. Looking forward, the growing market and increasing scientific interest indicate a potential expansion of SA-based products in aesthetic dermatology. Further clinical and mechanistic studies are required to validate its applications and establish standardized protocols for its use in cosmetic and medical settings. The aim of this article is to summarize the existing knowledge on the use of succinic acid in cosmetics and aesthetic dermatology. Despite the growing interest in this compound, no comprehensive review addressing its applications in these fields is currently available. Therefore, this work responds to this gap by integrating and discussing the limited but emerging scientific reports concerning the cosmetic and dermatological potential of succinic acid. Full article

Background: Human lead exposure is a multi-pathway phenomenon that integrates internal biological burden with persistent residential environmental reservoirs. Although individual lead metrics have been linked to cardiometabolic dysfunction, current research often fails to capture the ‘exposome’ reality of joint, nonlinear, and interaction-dependent effects on metabolic outcomes like BMI. Objectives: To evaluate associations between biological (blood and urinary) and residential dust (window and floor) lead measures and BMI, and to characterize nonlinear and interaction-dependent mixture effects using Bayesian Kernel Machine Regression (BKMR). Methods: We analyzed data from NHANES 2001–2002, a nationally representative survey of the U.S. noninstitutionalized civilian population. Window and floor dust lead (µg/ft²) were obtained from the NHANES household dust component, and blood lead (µg/dL) and urinary lead (µg/L) were measured using standardized NHANES laboratory protocols. BMI was calculated from measured height and weight. Missing data were addressed using multivariate imputation by chained equations. Descriptive statistics and multivariable linear regression were used to estimate adjusted associations between individual lead metrics and BMI, controlling for age, gender, income, race/ethnicity, and education. BKMR was then applied to evaluate joint mixture effects, estimate univariate and bivariate exposure–response functions, and quantify relative exposure importance using posterior inclusion probabilities (PIPs). Results: In covariate-adjusted linear regression, blood lead (β = −0.485; 95% CI: −0.566, −0.405; p < 0.001) and window dust lead (β = −0.00047; 95% CI: −0.00067, −0.00026; p < 0.001) were inversely associated with BMI, whereas floor dust lead was positively associated (β = 0.258; 95% CI: 0.209, 0.306; p < 0.001). Urinary lead was inversely but not significantly associated with BMI (β = −0.111; 95% CI: −0.235, 0.013; p = 0.079). In BKMR, blood lead was the dominant contributor, with a posterior inclusion probability (PIP; proportion of iterations in which an exposure is selected) of 1.00. Window dust lead showed modest inclusion (PIP = 0.26), whereas urinary and floor dust lead were not selected (PIP = 0.00). Exposure–response functions indicated modest nonlinearity for blood lead and greater divergence for the blood lead–window dust lead pairing at higher exposure levels. The overall mixture effect declined across increasing joint exposure quantiles, crossing the null near the median and becoming increasingly negative at higher mixture levels. Conclusions: In our study, lead metrics showed heterogeneous associations with BMI, and BKMR indicated that internal lead burden (blood lead) primarily drove mixture-related BMI patterns, with evidence that window dust lead may modify mixture effects at higher co-exposure levels. These findings support evaluating multiple lead exposure pathways jointly and using flexible mixture models to capture nonlinear and interaction-dependent relationships with BMI. Full article

Microplastics (MPs) are synthetic polymer particles that are generally less than 5 mm in size and have attracted heightened scrutiny due to their pervasive presence in the environment, along with their toxicological significance. Several research investigations documented its presence in humans as a profound finding in biological tissues and fluids crossing barriers, leading to oxidative and inflammatory pathways alterations associated with blood, placenta, cardiovascular, pulmonary, nephrotic, other systems, and their disorders. Given the ubiquitous utilization of microplastics across diverse sectors, it is imperative to systematically investigate and elucidate their potential toxicological effects on biological systems through rigorous and mechanistically informed research. This review will also provide the synthesis of recent mechanistic data on the toxicity that can be caused by MPs and will determine key gaps that impede efficient human health risk evaluation. A structured literature search was conducted via PubMed, Web of Science, and Scopus databases, mostly from the studies published between 2010 and 2026. The studies of exposure characteristics and biological effects were analyzed in vitro, in vivo, and in human biomonitoring, and the primary focus of the interventions includes oxidative stress, inflammation, apoptosis, hepatotoxicity, and metabolic malfunction. MPs possess various physicochemical properties, such as a low particle size, various shapes, surface area, polymer composition, and the presence of sorbed or intrinsic additives. When MPs are taken up by cells, they can induce oxidative stress via increasing ROS, eventually leading to high lipid peroxidation, mitochondrial malfunction, DNA fragmentation, and eventually cell death. MPs also cause pro-inflammatory cytokine responses, including TNF-α, IL-1β, and IL-6, altering the immune system and cell profile, leading to systemic inflammation. In aquatic and terrestrial organisms, these microplastics have a harmful impact on growth, reproduction, and behavior in a time- and dose-dependent manner. Under conditions of controlled exposure, the organ-specific toxicities that have been reported include hepatic, renal, neurological, reproductive, and cardiovascular systems. Although the fields of mechanistic knowledge are growing, there is still a substantial amount of uncertainty; there is a lack of characterization of the long-term effects of low-dose chronic exposure, the kinetics of bioaccumulation, biodegradation potential, and transgenerational effects. In addition, there are no standardized procedures for the characterization of MPs, nor the reporting of the distribution of size or exposure measurements, which limits the comparability of cross-studies and makes it difficult to assess risks quantitatively. The dynamics of interactions of MPs between co-adsorbed contaminants like heavy metals, polycyclic aromatic hydrocarbons, and endocrine-disrupting chemicals are also yet to be explored. Although all evidence available to date does indicate biologically plausible mechanisms of MP-induced toxicity, integrated research employing standardized analytical protocols, an environmentally relevant exposure model, and human epidemiological data is required to ensure that laboratory results are translated into evidence-based public health and regulatory actions. This review offers an in-depth analysis of the existing molecular understanding of MP-induced toxicity, demonstrates organism-level impacts throughout species, and establishes vital fields for future studies. In order to develop competent guidelines to minimize MP exposure and its adverse health effects, it is crucial to cover these gaps via research that incorporates toxicology and environmental science. Full article

Phenolic compounds are plant-derived antioxidants crucial for human health and food preservation. Their bioactive potential including anti-inflammatory, antimicrobial, and anti-carcinogenic properties makes them a vital focus in nutritional, pharmaceutical, and agricultural research. This review critically evaluates the methodologies for their extraction, detection, and quantification to accurately assess antioxidant activity. Oxidative stress in biological systems and food matrices necessitates accurate analytical methodologies for assessing antioxidant behavior, which include both in vitro, in vivo and ex vivo approaches. Sample pretreatment and extraction techniques are critical for reliable analysis and vary depending on the matrix, compound polarity, and target phenolic subclass. We compare conventional extraction techniques (Soxhlet, maceration) with advanced methods like ultrasound-assisted, microwave-assisted, and supercritical fluid extraction. Detection methods reviewed include spectrophotometric assays (e.g., DPPH, FRAP, ORAC), electrochemical sensors, and chromatographic techniques (e.g., HPLC, HPLC−MS). While each method has distinct advantages, a lack of standardization remains the primary challenge, driven by variations in protocols and the vast chemical diversity of phenolics. This review underscores the critical need for integrated, standardized approaches to ensure the accurate and comparable evaluation of antioxidant activity in research and industry. Full article