Search Results (907)

Cancer cachexia is a multifactorial systemic syndrome characterized by progressive muscle loss, with or without adipose tissue depletion, that cannot be reversed by conventional nutritional support. It affects cancer patients and is associated with reduced treatment tolerance, impaired physical function, poor quality of life, and increased mortality. The understanding of cachexia has evolved recently, from the perception of a simple nutritional disorder to a complex immune–metabolic syndrome, based on tumor–host interactions, systemic inflammation, metabolic dysregulation, and multi-organ dysfunction. This review summarizes the progression of cachexia research, highlighting key findings involving inflammatory cytokines, proteolytic pathways, mitochondrial dysfunction, and immune dysregulation. The development of therapeutic strategies is examined, from early nutritional and appetite-stimulating interventions to contemporary targeted therapies, including ghrelin receptor agonists, cytokine inhibitors, and anabolic agents. Despite advances in mechanistic understanding, numerous trials targeting single pathways have failed to produce meaningful functional or survival benefits, underscoring the limitations of reductionist approaches. Emerging evidence supports a paradigm shift toward multimodal, biomarker-guided, and patient-centered interventions that address the interconnected biological mechanisms underlying cachexia. Particular emphasis is given to novel immunomodulatory strategies, including agents such as R-ketorolac, which may restore immune homeostasis and target the root causes of cachexia. It is hypothesized that future therapeutic success will likely depend on integrated approaches combining immunological, metabolic, nutritional, and rehabilitative interventions. Full article

Background: Precision nutrition and nutrigenomics have emerged as promising strategies to personalize dietary interventions according to individual genetic, metabolic, and molecular characteristics. However, the potential of nutrigenomic-guided nutraceutical interventions to modulate biological aging pathways remains poorly characterized. Objective: This study aimed to map and synthesize the available evidence from human studies evaluating personalized nutraceutical interventions based on nutrigenomic approaches and their effects on biomarkers associated with biological aging. Methods: A scoping review was conducted following the Joanna Briggs Institute methodology and PRISMA-ScR guidelines. PubMed/MEDLINE, Embase, Scopus, and Web of Science were searched for studies published between 2021 and 2026. Eligible studies included adults aged ≥18 years receiving personalized nutraceutical interventions informed by genetic, genomic, metabolomic, or other molecular data. Outcomes included biomarkers of cellular aging, inflammation, oxidative stress, immunometabolism, and related pathways. Results: Twenty-one studies were included. Interventions involved omega-3 fatty acids, polyphenols, Nigella sativa, vitamin D, methyl-donor micronutrients, fermented papaya preparation, and other nutraceutical preparations and functional food-based interventions. Four major themes emerged: multi-omic stratification approaches, biological aging biomarkers, redox-inflammatory modulation, and applications in metabolic disorders. Most studies demonstrated favorable effects on intermediate molecular biomarkers, including inflammatory cytokines, oxidative stress markers, lipid metabolism, endothelial function, and DNA methylation signatures. However, evidence directly demonstrating slowing of biological aging through validated biomarkers such as epigenetic clocks or telomere dynamics remains limited. Conclusions: Personalized nutraceutical interventions exhibit biological plausibility for modulating pathways associated with healthy aging. Nevertheless, robust longitudinal studies incorporating validated biomarkers of biological aging and clinically meaningful outcomes are needed before their widespread implementation in precision medicine. Full article

Marked interindividual variability in drug response remains a major challenge in clinical pharmacology and cannot be fully explained by host genetics alone. Increasing evidence indicates that the gut microbiota constitutes an additional determinant of drug efficacy and toxicity through bidirectional interactions with pharmacological therapies. This recognition has led to the emergence of pharmacomicrobiomics, a field that investigates how microbial communities influence drug disposition and response, and how drugs, in turn, alter the microbiome. Microbiome-mediated effects on drug efficacy and toxicity have been described across several therapeutic areas, including oncology, multiple sclerosis, and type 2 diabetes mellitus. Although these findings are promising, most mechanistic evidence derives from preclinical and animal studies, with relatively limited validation in controlled clinical trials. Strategies to modulate the gut microbiota, including prebiotics, probiotics, and faecal microbiota transplantation, have shown preliminary promise in optimising drug efficacy and reducing adverse effects, although methodological heterogeneity and incomplete mechanistic understanding limit their current clinical application. The identification of robust microbiome-derived biomarkers and the integration of multi-omics approaches, particularly metabolomics, are expected to accelerate the translation of pharmacomicrobiomics into precision medicine. This review summarises current evidence regarding microbiome–drug interactions, the mechanisms underlying microbiome-mediated modulation of pharmacokinetics and pharmacodynamics, emerging therapeutic strategies, and the challenges that remain before pharmacomicrobiomics can be implemented in clinical practice. Full article

Microvascular invasion (MVI) is a critical prognostic indicator for recurrence and survival in hepatocellular carcinoma (HCC); however, its accurate preoperative assessment remains clinically challenging. Postoperative histopathology is subject to sampling bias and time delays, while traditional imaging techniques lack the molecular specificity required to predict MVI. Liquid biopsy, through the analysis of circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), circulating tumor RNA (ctRNA), and extracellular vesicles (EVs), provides a minimally invasive approach for capturing tumor-derived molecular and cellular signals associated with vascular invasion. This narrative review comprehensively summarizes the current evidence linking these four liquid biopsy analyte categories to MVI in HCC, evaluates their integration into multi-omics predictive models, including multi-marker, clinicopathological-integrated, and imaging-integrated strategies, and proposes an evidence-level framework that categorizes blood biomarkers according to the strength of their support for MVI prediction, distinguishing direct histopathological validation from indirect associations with aggressive tumor biology. Key challenges are critically examined, including the variable specificity of individual biomarkers for MVI, the lack of head-to-head comparative studies, the absence of standardized pre-analytical and analytical protocols, and the methodological limitations of current prediction models. As a narrative review, this work does not employ systematic review methodology, and the evidence synthesis should be interpreted accordingly. The review provides a framework for understanding how liquid biopsy-based MVI risk stratification may inform surgical and perioperative decision-making following prospective validation. Full article

Neurodegenerative diseases represent a major cause of disability and death, but early diagnosis, prognosis, and therapeutic monitoring are challenging due to biological heterogeneity and the absence of disease-specific biomarkers. Neurofilament light chain (NfL) is a highly sensitive fluid biomarker of neuroaxonal injury with well-established clinical utility in selected neurological disorders, especially in disease monitoring and prognostic evaluation. However, since NfL is not disease-specific, the interpretation has to be integrated with complementary molecular, imaging, and clinical biomarkers. Recent advances in genomics, epigenomics, transcriptomics, proteomics, metabolomics, microbiome profiling, and neuroimaging provide complementary information about the molecular and biological processes underlying neurodegeneration. Artificial intelligence (AI) and machine-learning approaches also allow the integration of these heterogeneous datasets for multimodal biomarker modeling. This review examines the biological and clinical relevance of NfL across major neurodegenerative diseases and critically discusses its combination with multi-omics, neuroimaging, and AI-based approaches. Special emphasis is placed on disease monitoring, prognosis, patient stratification, and therapeutic-response modeling, distinguishing established clinical applications from emerging research directions. The present review also addresses ongoing methodological challenges, including assay standardization, data harmonization, model interpretability, multicenter validation, and clinical translation. Finally, future potential is discussed for NfL-based multimodal biomarker frameworks in precision neurology. Full article

Thyroid cancer comprises a biologically diverse group of tumors initiated by a limited number of recurrent driver alterations, with additional molecular events promoting dedifferentiation, therapeutic resistance, and aggressive clinical behavior. Advances in tumor sequencing have clarified the molecular architecture of papillary, follicular, oncocytic, poorly differentiated, anaplastic, and medullary thyroid carcinomas, and have already changed management in selected settings. Molecular testing improves diagnostic refinement and risk assessment in cytologically indeterminate thyroid nodules, while alterations involving BRAF, RET, and NTRK can guide targeted therapy in advanced disease. Beyond DNA, transcriptomic, proteomic, epigenetic, metabolomic, immune, spatial, and liquid-biopsy approaches offer functional insight into differentiation state, treatment sensitivity, and resistance, although most remain investigational. Their clinical value depends not only on biological plausibility, but on reproducibility, incremental value beyond established clinicopathological variables, and the ability to alter patient management. Computational tools may further support integration of molecular and clinical data, but their usefulness likewise depends on calibration, external validation, interpretability, and demonstration of decision impact. This review synthesizes the genomic and multi-omics determinants of thyroid cancer management across diagnosis, risk stratification, treatment selection, resistance monitoring, and follow-up, and discusses the practical barriers that continue to limit routine implementation, including assay standardization, cost, access, and real-world feasibility. Progress in precision thyroid oncology will depend on robust validation of emerging biomarkers and clear evidence that they improve patient outcomes. Full article

Background: Peritoneal dialysis (PD) effluent of kidney failure patients represents an accessible liquid biopsy of the peritoneal cavity, yet the mechanisms determining its molecular composition remain poorly understood. We applied an integrative multi-omics approach to characterize the composition, transport dynamics, and molecular origin of the PD effluentome. Methods: Cell-free effluent, effluent cells, and plasma were collected from stable PD patients during standardized peritoneal equilibration tests in a randomized clinical trial. Targeted metabolomics, proteomics, and transcriptomic profiling were integrated with a reference human plasma proteome to investigate temporal molecular changes, peritoneal transport characteristics, and protein origin. Results: A total of 207 metabolites and 2970 proteins were identified in PD effluent. Metabolites exhibited distinct class-specific transport kinetics, with rapid equilibration of amino acids and biogenic amines, whereas lipids remained markedly underrepresented despite prolonged dwell times, indicating that transport is governed by physicochemical properties beyond molecular size alone. The effluent proteome underwent concordant alteration, with dwell time-dependent enrichment of pathways related to extracellular matrix organization, angiogenesis, coagulation, and tissue repair. Integrative analysis of the effluent proteome, effluent-cell transcriptome, and human plasma proteome resolved distinct plasma-associated, effluent cell-associated, resident peritoneal tissue-associated, and mixed-origin protein populations. Conclusions: This study establishes the first systems-level approach describing the composition, transport dynamics, and molecular origin of the PD effluentome. By transforming PD effluent into a biologically interpretable molecular readout of peritoneal membrane biology, this work provides a reference for the mechanistic interpretation of effluent-derived biomarkers and supports future therapeutic monitoring and precision medicine in PD. Full article

Diffusion-weighted imaging (DWI) has evolved from a niche research sequence into an increasingly valuable adjunct to conventional magnetic resonance imaging (MRI) in musculoskeletal (MSK) radiology. By providing qualitative and quantitative information on tissue microstructure through assessment of water diffusion and apparent diffusion coefficient (ADC) mapping, DWI offers functional insights beyond conventional morphological imaging. We aim to present the current evidence for DWI in MSK imaging organised around established applications and emerging applications, with particular emphasis on composition-related interpretive pitfalls relevant to differentiating tumours and other pathologies, and to review the technique’s evolving role in routine practice. This narrative review synthesises the current literature on the clinical utility of DWI in MSK imaging. It is structured in four parts: foundations and the tissue composition signal framework, including the basis of qualitative and quantitative assessment; established applications; emerging applications; and assessment of tissue composition-related interpretive as well as technical pitfalls, including those arising due to myxoid matrix, chondroid matrix, blood degradation products, organising thrombus, crystalline or mineralised material, keratinaceous debris, purulent content, cellular haematopoietic marrow, by using original cases from the authors’ institution, which have been confirmed either histologically or surgically. Applications are stratified by strength of evidence. Established applications of DWI include soft tissue abscess detection, differentiation of malignant from benign soft tissue tumours, differentiation of malignant from benign vertebral compression fractures, and myeloma staging and response assessment, as well as treatment response in soft tissue and bone sarcomas. Whole-body MRI with DWI for staging and response assessment in multiple myeloma is guideline-endorsed and supported by prospective multicentre data. Soft tissue abscess detection, soft tissue and bone tumour characterisation, and characterisation of vertebral compression fractures are supported by consistent evidence from multiple independent cohorts, although no universally transferable ADC threshold exists. The emerging applications, which are promising adjuncts supported by small, single-centre or heterogeneous studies with thresholds that have not been externally validated, include ADC ghost sign in osteomyelitis (high specificity but sensitivity of only 20%), peripheral nerve sheath tumour characterisation and surveillance in NF1 patients, peripheral neuropathy and plexopathy, predisposing conditions such as Li Fraumeni syndrome in paediatric cancers, inflammatory myopathy, and postsurgical assessment of residual disease, as well as opportunistic detection of venous thrombosis. Radiomics and machine learning approaches remain experimental. Recent technical advances, including reduced field-of-view imaging, multi-shot acquisition and improved fat suppression, have mitigated but not eliminated historical limitations of susceptibility artefacts and limited spatial resolution. DWI has become an important functional imaging technique that complements conventional MRI across a broad range of musculoskeletal disorders. Understanding the relationship between tissue composition and the diffusion signal is central to both interpreting DWI correctly and avoiding its characteristic pitfalls. DWI is best regarded not as a stand-alone technique but as one component of a multiparametric assessment, in which its functional information is integrated with conventional morphological imaging. Ongoing technical improvement and expanding clinical evidence are expected to further support its integration into routine MSK imaging and its development as a quantitative biomarker for diagnosis, prognostication, and treatment monitoring. Full article

Hair follicle homeostasis is influenced by hormonal pathways, the scalp microenvironment, and environmental stressors such as pollution, UV radiation, and oxidative stress. Elissara^®, a polyphenol-enriched botanical ingredient, has shown benefits for scalp moisturization, barrier function, sebum regulation, and redness. Building on these scalp-level benefits, we investigated Elissara’s effects on follicular signaling, survival-associated biomarkers, oxidative damage, and androgen-related pathways as potential contributors to follicular health, using in silico, in vitro, and ex vivo models. Molecular docking (AutoDock Vina) of the main Elissara bioactives (oleuropein, hydroxytyrosol, verbascoside, carnosic acid, carnosol, and quercetin) identified SRD5A2 as a favorable predicted target, with individual binding energies ranging from −8.70 to −9.73 kcal/mol, approaching finasteride/dutasteride reference values. As an exploratory approach, simultaneous multi-ligand docking showed favorable global docking outputs for several targets, indicating that multiple bioactives could be structurally accommodated within complementary regions of the binding site. In human follicle dermal papilla cells, Elissara significantly increased BrdU incorporation to 245.70% of control at 0.002% and reduced SRD5A2 protein levels by 18.48% at 0.006%. In human scalp explants, Elissara at 200 µg/mL increased β-catenin, Bcl-2, and collagen IV under basal conditions and counteracted acute PM_2.5/UVA-induced alterations in β-catenin, Ki67-positive cells, Bcl-2, IGF-1, collagen IV, and protein carbonylation. Together, these findings support the potential of Elissara as a promising nutricosmetic ingredient for supporting follicular resilience through multiple follicle-relevant pathways. Clinical studies assessing hair growth outcomes are needed to determine whether these preclinical findings translate into measurable benefits. Full article

Despite major advances in reperfusion strategies and guideline-directed medical therapy, patients surviving myocardial infarction (MI) remain at substantial risk of recurrent cardiovascular events. Among the multiple determinants of post-MI cardiovascular risk, persistent atherothrombotic vulnerability remains a major therapeutic challenge despite guideline-directed secondary prevention. This review provides an integrated overview of residual atherothrombotic risk after MI, focusing on the complementary roles of lipid, inflammatory, and thrombotic pathways. Current evidence supports the use of biomarkers such as apolipoprotein B, lipoprotein(a), remnant cholesterol, triglyceride-rich lipoproteins, and high-sensitivity C-reactive protein to improve risk stratification beyond LDL-C. Landmark clinical trials have demonstrated that intensive lipid-lowering therapy, selected anti-inflammatory agents, and individualized antithrombotic strategies can further reduce recurrent cardiovascular events in appropriately selected patients. However, these pathogenic pathways rarely occur in isolation and frequently overlap, generating heterogeneous residual risk phenotypes. We propose a pragmatic multi-layered model in which lipid, inflammatory, and thrombotic mechanisms are viewed as interconnected biological processes rather than independent entities. This framework supports a personalized approach to secondary prevention based on comprehensive risk assessment, targeted therapeutic intensification, and longitudinal reassessment. Integrating these complementary domains may provide a framework for more individualized secondary prevention after MI, although its clinical utility requires prospective validation. Full article

Lead (Pb) is a widespread environmental pollutant that induces systemic toxicity in aquatic organisms primarily through oxidative stress, hematological disruption, and immune dysfunction. This study investigated the protective effects of ellagic acid (EA) against Pb-induced toxicity in common carp (Cyprinus carpio) using a multi-biomarker approach and molecular docking. Fish were assigned to six experimental groups: control, EA-treated, Pb-I, Pb-I + EA, Pb-II, and Pb-II + EA. Fish in the Pb-I and Pb-II groups were exposed to 2.5 and 5 mg/L Pb, respectively, while EA was administered via diet at 100 mg/kg for 14 days. At the end of the exposure period, hematological indices, innate immune parameters, and oxidative stress biomarkers were assessed in blood, liver, kidney, and gill tissues. Pb exposure caused marked hematological impairment, suppressed immune responses, increased malondialdehyde levels, and disrupted antioxidant defense by reducing SOD, CAT, GSH-Px, and GSH levels while increasing GST activity. In contrast, dietary EA supplementation significantly mitigated Pb-induced alterations, improved hematological and immunological responses, reduced lipid peroxidation, restored antioxidant capacity, and normalized GST activity to control levels. Molecular docking analyses further showed that EA interacts with hemoglobin and immunoglobulin M, supporting its potential role in preserving oxygen transport and immune functions under Pb-induced stress. Overall, the findings demonstrate that Pb-induced toxicity involves coordinated disruption of redox homeostasis and immune function, whereas EA exerts a multi-target protective effect through biochemical and molecular mechanisms. This study provides mechanistic insight into chemical–biological interactions and supports the potential application of natural bioactive compounds in mitigating heavy metal-induced toxicity. Full article

Psoriasis vulgaris is a chronic immune-mediated inflammatory disease in which treatment response is assessed using clinical indices such as the Psoriasis Area and Severity Index (PASI) and Dermatology Life Quality Index (DLQI), despite their limited ability to capture systemic inflammation and underlying immunological activity. This narrative review aims to summarize current evidence on inflammatory biomarkers for monitoring treatment response and to evaluate their potential clinical utility. A structured, non-systematic literature search was performed in April–May 2026 across PubMed, Cochrane Library, Scopus, and ClinicalTrials.gov, focusing primarily on literature published during the preceding 10 years, with selected earlier studies being retained when directly relevant. Emerging data indicate that multiple biomarker domains may reflect therapeutic outcomes, including cytokines and chemokines, acute-phase proteins, complete-blood-count (CBC)-derived inflammatory indices, genetic markers, micro(mi)RNAs, metabolomic and lipidomic profiles, and tissue-based markers. These biomarkers may serve as severity-associated, baseline-predictive, pharmacodynamic, or prognostic markers, and these roles should not be interpreted interchangeably. Nevertheless, discrepancies between biomarker dynamics and clinical improvement occur, reflecting the partial dissociation between local and systemic inflammation, disease heterogeneity, and differences in response kinetics. Although inflammatory biomarkers provide a biologically grounded framework for assessing treatment response, their clinical implementation remains limited. Further large-scale, standardized studies are required to validate candidate markers and support the development of integrated, multi-omics approaches for personalized management. Full article

Despite substantial progress in biomarker discovery and multi-omics profiling, several features of Alzheimer’s disease (AD), including prolonged compensated states, heterogeneous clinical trajectories, and marked stage-dependent therapeutic responses, remain difficult to integrate into a single mechanistic framework. In this review, we propose an integrative and testable conceptual framework that reframes AD as a single, progressive multi-layer network disorder whose dynamics arise from hierarchical constraint propagation and progressive loss of cross-scale coordination. Integrating evidence from human genetics, epigenomics, transcriptomics, proteomics, metabolomics, spatial biology, connectomics, and longitudinal biomarker studies, we examine how molecular, cellular, and circuit-level processes interact over time to shape disease progression. Within this framework, different omics measurements are interpreted as complementary representations of disease-related changes, rather than as independent molecular signatures. Disease progression reflects the gradual convergence of immune, metabolic, proteostatic, cytoskeletal, and synaptic stress, with overt cognitive impairment emerging when compensatory capacity is exceeded, producing threshold-like network destabilization. By explicitly linking biological scale, temporal hierarchy, and network structure, this synthesis extends prior network-medicine, connectomic, and multi-omics approaches into a testable framework for state-aware stratification, integrative analysis, and stage-appropriate therapeutic investigation in AD. Full article

Antibody–drug conjugates (ADCs) targeting HER2 (encoded by ERBB2), Trop2 (TACSTD2), and Nectin-4 (NECTIN4) have demonstrated clinical activity in bladder cancer. However, the co-expression relationships among these three targets at both the transcriptomic and protein levels, and their spatial organization within the tumor microenvironment, remain incompletely characterized. To address this, we performed a multi-platform, multi-resolution analysis integrating ten bulk RNA-seq datasets (n = 1628 samples), six spatial transcriptomics datasets (10x Visium and Visium HD, n = 31 specimens), and multiplex immunofluorescence (mIF) staining on three paired tumor–adjacent normal bladder specimens. Pairwise Pearson correlation analyses were conducted for ERBB2, TACSTD2, and NECTIN4 across bulk transcriptomes and spatial compartments (tumor core, interface, and stroma). High-resolution co-expression hotspot mapping was performed using Visium HD data. Preliminary protein-level evidence was obtained using mIF, with quantitative three-dimensional surface plot reconstructions illustrating the spatial distribution of marker co-expression. Given the limited number of specimens (three paired tumor–normal samples), these findings should be regarded as exploratory and illustrative rather than definitive validation. Moderate-to-strong positive correlations among the three genes were consistently observed across all ten bulk datasets, with the strongest association between TACSTD2 and NECTIN4 (Pearson r up to 0.969). Spatial transcriptomic analyses recapitulated these positive correlations across all histopathological compartments, with the tumor core generally exhibiting the highest coefficients. No consistent spatial gradient was observed, indicating preservation of co-expression across tissue niches. Visium HD analysis confirmed positive pairwise correlations and identified discrete co-expression hotspots. mIF revealed extensive Trop2–Nectin-4 protein colocalization across all tumors, whereas HER2 showed more restricted and heterogeneous expression. Triple-positive subpopulations were identified but were limited. Strikingly, all three markers exhibited pronounced tumor-specific upregulation relative to paired adjacent normal tissues, with surface plot analysis showing markedly elevated cumulative fluorescence intensities in tumors (Z-values approaching 240 arbitrary units) compared with a flattened landscape in normal tissues (Z-values < 100–120 arbitrary units). These findings provide a multi-scale description of ADC-target co-expression in bladder cancer and generate hypotheses for biomarker-driven patient stratification, which require validation in larger, independent cohorts. Full article

Purpose: Acute exercise elicits coordinated neuroendocrine and autonomic responses that are strongly influenced by exercise modality and intensity. While cortisol and salivary alpha-amylase are widely used as peripheral indicators of hypothalamic–pituitary–adrenal (HPA) axis and sympathetic nervous system activation, respectively, salivary orexin remains an exploratory biomarker whose relationship with central orexinergic activity is not yet fully established, particularly in response to different exercise conditions. This study aimed to compare the acute salivary responses of orexin, alpha-amylase, and cortisol following distinct exercise modalities characterized by different physiological loads. Methods: A repeated-measures design was employed in a sample of physically active young women. Each participant completed four experimental sessions: high-intensity interval training (HIIT), combined exercise (CE), moderate-intensity continuous training (MICT), and yoga. Saliva samples were collected at baseline (T0), immediately post-exercise (T1), and during early recovery (T2 and T3). Biomarker concentrations were analyzed to assess temporal dynamics and modality-dependent differences. Results: All biomarkers showed modality-dependent responses. HIIT elicited the most pronounced increases in alpha-amylase and cortisol, reflecting robust sympathetic and HPA axis activation, whereas CE and MICT induced more moderate responses. Yoga was associated with minimal changes across all biomarkers. Orexin displayed a graded response across the exercise modalities, with higher post-exercise levels observed following HIIT compared to lower-intensity conditions. Temporal analyses revealed distinct kinetic profiles, with alpha-amylase peaking rapidly in the early post-exercise phase, whereas cortisol exhibited a more delayed response. Conclusions: These findings demonstrate that acute salivary responses of orexin, alpha-amylase, and cortisol are strongly modulated by exercise modality, supporting the importance of an integrated, multi-biomarker approach to characterize the physiological response to exercise. The inclusion of salivary orexin provides exploratory information on a potentially relevant arousal- and energy-regulation-related pathway, complementing established peripheral markers of autonomic and endocrine activation. This integrated perspective may contribute to a more comprehensive understanding of exercise-induced stress responses in young women. Full article