Search Results (704)

Sarcopenia is an age-related syndrome characterized by the progressive loss of skeletal muscle mass, strength, and function, significantly impairing older adults’ independence and quality of life. Given their anti-inflammatory, antioxidant, and metabolic regulatory properties, n-3 polyunsaturated fatty acids (n-3 PUFAs) have emerged as a promising nutritional strategy to mitigate this muscle degeneration. This review systematically synthesizes existing evidence regarding the association between n-3 PUFAs and sarcopenia. To capture the relevant literature, we searched PubMed, Web of Science, CNKI, and Wanfang Data using a combination of subject headings and free-text terms. We supplemented primary search terms—such as “n-3 polyunsaturated fatty acids,” “omega-3 fatty acids,” “sarcopenia,” and “muscle mass”—with mechanism-related keywords like “inflammation,” “muscle satellite cells,” and “oxidative stress.” We also manually screened the reference lists of the included literature. Our inclusion criteria encompassed interventional studies, observational studies, and high-quality reviews, while excluding conference abstracts, duplicate publications, and studies with incomplete data. This review first outlines the established biological mechanisms linking n-3 PUFAs to the pathological progression of sarcopenia, specifically detailing how these fatty acids improve muscle satellite cell function, suppress inflammation and oxidative stress, and ameliorate metabolic disorders. Next, we critically evaluate recent clinical studies and reviews, analyzing sources of study heterogeneity such as variations in sample size, intervention dose and duration, outcome measures, and baseline participant characteristics. We also highlight current research hotspots—including specialized pro-resolving mediators (SPMs), the gut–organ axis, combined interventions, and precision nutrition strategies—while emphasizing the functional differences between EPA and DHA to guide future intervention designs. Current evidence indicates that while n-3 PUFA supplementation can improve muscle strength and physical performance in older adults, its effects on muscle mass remain inconsistent. Addressing key research gaps, particularly the lack of standardized core outcome measures and unclear dose–response relationships, is critical. Ultimately, future research must prioritize developing high-bioavailability formulations, conducting personalized trials based on baseline n-3 PUFA status, and deepening investigations into inter-organ networks to translate these nutritional insights into effective sarcopenia prevention and management strategies. Full article

Recyclers worldwide face a common bottleneck: incoming mixed plastic bales are chemically opaque, yet the choice between mechanical recycling, chemical recycling, and energy recovery hinges on contaminant levels that cannot be judged by visual inspection alone. This study develops and validates a tiered analytical decision-support framework that translates standard laboratory measurements into explicit, actionable go/no-go routing criteria for any mixed polyolefin waste stream. The framework is organized into three successive analytical tiers of increasing specificity: Tier 1 uses FTIR and DSC for rapid polymer identification and thermal subclass confirmation; Tier 2 applies TGA/DTG for thermal stability assessment and filler quantification; and Tier 3 deploys ICP-OES, WD-XRF, CIC, and TG–MS for targeted heavy metal, halogen, and evolved gas profiling, triggered only when Tier 1/2 flags are raised. This staged logic minimizes unnecessary testing while ensuring that contaminant-relevant information is captured where it matters. The framework is demonstrated on nine blind mixed plastic waste streams (P1–P9) supplied by an industrial recycling facility without prior disclosure of polymer identity, filler content, or additive history—conditions that replicate the uncertainty encountered at any sorting plant globally. Application of the tiered protocol identified dominant polymers (HDPE, LDPE, PP), quantified inorganic fillers (CaCO₃ up to ~38 wt%), and detected hazardous contaminants, including chlorine (up to ~1900 ppm), lead, chromium, and titanium, enabling each stream to be assigned to a specific recycling route with defined contaminant thresholds. Because the method relies exclusively on commercially available, vendor-independent instrumentation and follows a reproducible, rule-based decision logic, it is directly transferable to recycling facilities in any geographic context without site-specific calibration. The proposed framework thus provides a practical, scalable decision-support tool for feedstock-level quality control under emerging regulations such as the UNEP Global Plastics Treaty. Full article

This study explores how Artificial Intelligence (AI) shapes Green Purchasing Behavior through cognitive and attitudinal mechanisms by implementing the Stimulus–Organism–Response (SOR Model) Theory. It analyzes AI as an external stimulus that influences Environmental Knowledge and Green Truth, which, in turn, affects Environmental Attitudes and Green Purchasing Behavior. A cross-sectional quantitative design was employed using survey data collected from 412 consumers in the province of Guayas (Ecuador). The data were analyzed using partial least-squares structural equation modeling (PLS-SEM). The results indicate that AI exerts a weak influence on Green Purchasing Behavior; instead, its impact operates primarily through indirect pathways. Specifically, AI significantly enhances Environmental Knowledge and promotes Green Truth, subsequently shaping consumers’ Environmental Attitudes. Furthermore, Environmental Attitude emerged as the strongest predictor of Green Purchasing Behavior, confirming its central role in translating internal evaluations into consumption decisions. These findings contribute to the literature by integrating AI into sustainable consumption models and demonstrate that its effectiveness depends on its ability to generate credible and meaningful internal responses rather than directly influencing behavior. Full article

Prostate cancer (PCa) is one of the leading causes of cancer-related morbidity and mortality in men worldwide, and early detection remains crucial for ensuring effective treatment and improving patient outcomes. In this context, the development of non-invasive, accurate, and cost-effective screening strategies is of paramount importance. One particularly promising and innovative approach is the analysis of volatile organic compounds (VOCs), a field known as volatolomics. VOCs, which are metabolic by products released by the body, reflect underlying biochemical processes and offer a valuable, non-invasive source of diagnostic information. Recent advances have highlighted the potential of VOC profiling in PCa detection. A variety of biological systems have demonstrated remarkable sensitivity and specificity in recognizing disease-associated VOC signatures. Notably, trained dogs, selected invertebrates, and artificial sensing platforms have all shown the ability to identify PCa-related olfactory patterns. Among technological approaches, electronic noses (eNoses), which combine chemical sensor arrays with pattern recognition algorithms such as neural networks, represent a rapidly evolving diagnostic tool. Together, these biologically inspired and technology-driven strategies are reshaping the landscape of cancer diagnostics. They offer a compelling foundation for the development of rapid, non-invasive, and clinically translatable methods for PCa detection. This narrative review summarizes recent advances in using VOCs for PCa diagnosis and evaluates the reproducibility and clinical robustness of these approaches, focusing on challenges such as standardizing sampling, storage, and analysis, small cohort sizes, and the need for external validation and regulatory integration. Full article

Blood disorders encompass a wide range of diseases including anemia, hemophilia, thrombotic disorders, platelet dysfunction, and hematological cancers, making blood disorders a major global health concern. These conditions can impair processes vital to human physiology including oxygenation, coagulation, and immune defense. Hematologic malignancies, both chronic and acute, require timely diagnosis and ongoing disease monitoring for effective clinical management. Microfluidic technologies have emerged as promising alternatives to benchtop techniques for diagnosing and monitoring hematological disorders. For example, microfluidic assays can be used for the isolation and characterization of liquid biopsy markers such as rare cells, extracellular vesicles, and cell-free molecules to support disease management in a minimally invasive manner while the process automation afforded by microfluidics decentralizes healthcare, making it more accessible. Advances in lab-on-a-chip technologies, including large-scale fabrication methods and novel design strategies, will provide tools for the clinical validation of biomarkers and the translation of these technologies from the laboratory bench to the patient bedside. In this review, we will show that microfluidic devices enable disease monitoring via high-throughput analysis of liquid biopsy samples for the detection of rare disease-specific biomarkers found in blood, plasma, urine, etc., providing an alternative to standard benchtop testing using specimens secured via invasive bone marrow procedures, typically used for managing blood-based diseases. A key advantage of microfluidics is their ability to manipulate blood components at scales that closely mimic the body’s microvascular environment. Not surprisingly, microfluidic vascular models have been developed to replicate physiological rheology enabling quantitative assessment of blood cell deformability, aggregation, or clot formation. We provide a critical perspective on the use of the microfluidic “organ-on-chip” designed for blood disorders’ modeling and employed to recapitulate the blood cancer microenvironment. A summary of advances in microfluidic strategies for detection, diagnosis, drug screening, and mechanistic investigations of blood disorders, and future directions for precision testing, will be presented. Full article

Electrochemical sensors have emerged as promising tools for rapid pesticide screening in food and environmental samples because they combine simple instrumentation, fast response, portability, and compatibility with disposable electrodes. This review organizes recent progress through a cross-system framework linking pesticide class, interfacial electrochemical process, and material design. Carbon materials, metal–organic frameworks and their derivatives, metal nanoparticles, metal compounds, conducting polymers, MXene-based composites, and selected emerging materials are compared in terms of enrichment capability, charge-transfer regulation, catalytic amplification, recognition-layer integration, and suitability for real-sample analysis. Emphasis is placed on issues that are often under-discussed in performance-centered surveys, including matrix interference, electrode fouling, batch-to-batch reproducibility, storage stability, scalability, and cost-effectiveness. Representative examples show that the most useful advances arise not simply from lowering the limit of detection but from improving structure–function understanding and translating interfacial design into robust analytical performance. Future work should prioritize standardized fabrication and benchmarking protocols, in situ and operando identification of active sites and interface evolution, matrix-specific antifouling validation, multiresidue and metabolite analysis, and hybrid portable devices coupled with intelligent readout. Full article

Background/Objectives: Hand hygiene is a cornerstone of infection prevention, yet the extent to which multimodal institutional hand hygiene interventions translate into measurable reductions in healthcare-associated infections (HAIs) remains uncertain. This systematic review aimed to evaluate the association between hospital-wide or multi-ward multimodal hand hygiene interventions and clinical HAI outcomes in acute care hospitals. Methods: A structured literature search was conducted in PubMed, Scopus, Embase, and Google Scholar using a combination of Medical Subject Headings (MeSH) and free-text terms related to hand hygiene, healthcare-associated infections, hospital settings, and intervention strategies. Eligible studies were quasi-experimental designs, including before–after, controlled before–after, and interrupted time-series studies, evaluating multimodal hand hygiene interventions implemented at hospital-wide or multi-ward level and reporting clinical HAI outcomes. Two reviewers independently assessed risk of bias using the ROBINS-I tool, and certainty of evidence across major outcome categories was summarized using GRADE. Results: twelve studies met the inclusion criteria. Overall, multimodal hand hygiene interventions were generally associated with favorable directional trends in clinical outcomes. Reductions were most consistent for broader institutional HAI measures and some device-associated infections, particularly central line-associated bloodstream infections. In contrast, organism-specific outcomes, including methicillin-resistant Staphylococcus aureus, vancomycin-resistant Enterococcus, and Clostridioides difficile, were more heterogeneous across studies and settings. All included studies were judged to be at serious or critical overall risk of bias, primarily because of confounding, lack of contemporaneous controls, co-interventions, and phased implementation. Conclusions: Multimodal hand hygiene programs in acute care hospitals may be associated with improvement in selected clinically relevant HAI outcomes, particularly at the institutional level. However, the overall certainty of evidence remains low to very low, and the strength of inference is limited by the non-randomized nature of the available studies and the difficulty of isolating the independent effect of hand hygiene within complex infection-prevention strategies. Full article

Background/Objectives: Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by complex and interconnected pathophysiological mechanisms, including mitochondrial dysfunction, oxidative stress, neuroinflammation, lysosomal impairment, and altered neurotransmitter metabolism. Unlike cerebrospinal fluid or blood, urine offers a truly non-invasive source of biomarkers, reflecting systemic metabolic changes and renal protein excretion linked to neurodegeneration. This review aims to critically synthesize current evidence on urinary biomarkers in PD and to organize this heterogeneous literature into pathophysiologically meaningful domains. Methods: A comprehensive literature search of human studies investigating urinary biomarkers in PD was performed. Eligible studies were comprehensively analyzed and classified according to dominant biological pathways. To facilitate interpretation, findings were organized into six thematic domains: genetic and protein-based biomarkers; metabolic pathways and mitochondrial dysfunction; oxidative stress and neuroinflammation; gut–brain-axis-related metabolites; hormonal and systemic biomarkers; and emerging exploratory markers. Results were summarized in domain-specific tables and integrated using a conceptual framework. Results: A total of 32 human studies met the inclusion criteria, revealing diverse urinary molecular signatures associated with PD across multiple biological domains. Genetic and protein-based markers, including LRRK2-related proteins, α-synuclein species, and lysosomal lipids, showed potential for disease stratification. Metabolomic studies consistently identified alterations in acylcarnitines, organic acids, and amino acid metabolism, reflecting mitochondrial dysfunction. Biomarkers related to oxidative stress, immune activation, gut microbiota metabolism, and hormonal regulation further highlighted the systemic nature of PD. However, most individual biomarkers lacked disease specificity and exhibited methodological heterogeneity. Conclusions: Current evidence supports urine as a valuable source of systemic biomarkers reflecting multiple pathophysiological processes in PD. While single urinary markers remain insufficient for clinical application, integrated omics-based approaches—particularly metabolomics and peptidomics/proteomics—hold promise for identifying combinatorial biomarker signatures. Future longitudinal and standardized studies are required to enhance specificity and translational potential for non-invasive diagnosis and disease monitoring in PD. Full article

Cancer continues to present a profound challenge due to high mortality and the inherent limitations of conventional treatments, including suboptimal targeting, systemic toxicity, and difficulty in overcoming physiological barriers. Micro/nanorobots (MNRs) offer a promising enhanced precision and efficacy in cancer therapy. This review systematically analyzes recent advancements in MNR applications, establishing a consistent framework that interlinks their diverse material compositions, propulsion strategies, and therapeutic functions. We critically compare various materials (inorganic, organic/polymeric, and biological/hybrid materials), elucidating their respective trade-offs in biocompatibility, biodegradability, and stimulus responsiveness. This paper further examines both internal (chemical and biological) and external (magnetic, light, and ultrasound) propulsion mechanisms, highlighting their strengths in overcoming biological barriers and enabling complex in vivo navigation, while also discussing their inherent limitations in control, fuel dependency, and tissue penetration. We then synthesize the therapeutic capabilities of MNRs across targeted drug delivery, phototherapy, radiotherapy, and immunotherapy, emphasizing common advantages like enhanced tumor specificity and reduced systemic side effects. A forward-looking perspective was also provided on the remaining challenges, particularly focusing on in vivo controllability, long-term biosafety, manufacturing scalability, and the significant hurdles in clinical translation. By offering a more critical and integrated analysis, this review underscores the immense potential of MNRs to revolutionize personalized precision cancer treatment, while candidly addressing the complex obstacles that must be surmounted for their successful clinical adoption. Full article

Protein synthesis is a crucial biosynthetic process in all organisms, including plants. The integrity of the translational machinery, especially ribosomes, can be compromised during rapid cell division in ontogenesis or in response to environmental stress. In this study, Northern blotting was employed to analyze total RNA from various angiosperms, focusing on small 5′- and 3′-terminal 18S rRNA fragments. Stem-loop array RT-PCR was employed to map the cleavage sites within the target regions. Severe stress, such as extreme drought, induced the accumulation of three distinct 18S rRNA fragments across diverse angiosperm taxa, indicating that this phenomenon is likely universal. In rapidly dividing cells, such as those found in in vitro callus cultures and germinating wheat embryos, high levels of discrete 5′-terminal fragments were observed, while 3′-terminal fragments were absent. The stem-loop array RT-PCR mapping identified specific sites of 18S rRNA strand breaks. Structural annotation of the 3D model of the plant 40S subunit revealed spatial clustering of these sites in proximity to the RPS6 binding region. Notably, wheat cultivars that are tolerant to osmotic stress exhibited significantly higher levels of 18S rRNA fragmentation than sensitive cultivars. This suggests a regulatory mechanism rather than a mere byproduct of apoptotic-like regulated cell death. Additionally, fragmented ribosomes were gradually eliminated during embryo maturation, indicating a process of programmed functional ribophagy. Our findings suggest that a potential inability of plant tissues to selectively retain functional ribosomes might contribute to a decline in generative potential. Monitoring the integrity of the translational machinery could improve breeding efficiency and aid in preserving long-term stored germplasm. Full article

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a complex, multi-system disease characterized by a multitude of symptoms across various organ systems. Diagnosis has relied heavily on heterogeneous clinical symptom presentation and evolving case definitions, with treatment focused on addressing presenting symptoms due to the paucity of validated biomarkers. Meanwhile, advances have been made in understanding the underlying pathophysiology through strong epidemiologic, clinical, and basic science studies. This narrative review synthesizes recent advances that are likely to drive a shift in understanding from symptom-based classification toward a molecularly defined understanding of the disease. This shift in understanding will likely provide the foundation for future research efforts focused on targeting diagnosis and treatment more effectively. Specifically, we reference the identification of rare genetic risk variants through the HEAL2 deep learning framework, the large-scale DecodeME genome-wide association study, and dynamic epigenetic markers of disease state. In addition, the findings revealed the downstream consequences of this genetic and epigenetic priming: chronic innate immune activation, CD8+ T cell exhaustion characterized by upregulation of the exhaustion-driving transcription factors Thymocyte Selection-Associated HMG Box (TOX) and Eomesodermin (EOMES), and a cellular energy crisis centered on mitochondrial dysfunction. Furthermore, results of recent studies have revealed sex-specific transcriptomic and proteomic signatures of maladaptive recovery. We also highlight the role of machine learning and artificial intelligence integrations in translating high-dimensional multi-omics data into actionable biological insights, including the identification of monocyte subsets via Positive Unlabeled Learning, circulating cell-free RNA diagnostic signatures, and integrated multi-modal disease models such as BioMapAI. The combination of these findings, which highlight multiple identifiable mechanisms of molecular activity, support the feasibility of molecular subtyping, precision diagnostics, and targeted therapeutic strategies for ME/CFS. Full article

(1) Background: Glyphosate (GLY) and glyphosate-based herbicides (GBHs) are widely used agrochemicals. Experimental studies have reported oxidative stress, inflammatory activation, mitochondrial impairment, endocrine-related effects, and organ injury following GLY/GBH exposure; however, candidate mitigation approaches have not been comprehensively summarized across experimental systems. (2) Methods: This structured narrative review followed SANRA recommendations. PubMed, Scopus, Web of Science, and Embase were searched (January 2004–January 2026). In total, 37 experimental studies met the inclusion criteria, describing 23 compounds categorized as vitamins, antioxidants, or enzyme modulators, dietary supplements, plant extracts, humic substances, hormonal modulators, and other natural compounds. (3) Results: Across models, reported protective effects most consistently involved attenuation of oxidative damage, including reductions in lipid peroxidation, oxidative DNA damage markers, and partial restoration of endogenous antioxidant defenses. Several interventions also modulated inflammatory signaling, apoptosis-associated markers, and stress response signaling. Protective effects were generally dose-dependent and more frequently observed in pre-treatment or co-exposure paradigms; complete normalization of outcomes was uncommon. Interpretation across studies was limited by heterogeneity in exposure conditions, test systems, endpoints, and, critically, by differences between pure GLY and GBHs. (4) Conclusions: Experimental evidence supports the mechanistic plausibility of antioxidant and stress response modulation as candidate approaches to mitigate GLY/GBH-induced toxicity. However, substantial methodological variability, frequent use of high-dose or non-representative exposure paradigms, and the absence of human interventional data limit translational relevance. Future studies should prioritize standardized, formulation-specific designs with exposure scenarios aligned to real-world conditions and include systematic safety assessment of proposed interventions. Full article

Decidualization in the human endometrium is not merely a hormone-dependent differentiation process; rather, it represents a multilayered adaptive program characterized by the tight integration of immune regulation, metabolic reprogramming, and cellular stress responses. In this review, endoplasmic reticulum (ER) stress and the associated unfolded protein response (UPR) are proposed as central regulatory mechanisms governing this process. Triggered by increased protein synthesis and secretory demand, UPR activation under physiological conditions preserves proteostasis and supports the secretory capacity of stromal cells. In contrast, chronic or dysregulated activation leads to a maladaptive response characterized by apoptosis, inflammation, and metabolic dysfunction. UPR signaling pathways shape immune tolerance through their effects on macrophage polarization, uterine natural killer (uNK) cell function, and T cell balance. At the metabolic level, adenosine monophosphate-activated protein kinase (AMPK) regulates cellular adaptation through bidirectional interactions with mitochondrial function and redox homeostasis. Within this framework, the ER stress–immune–metabolic axis operates not as a linear pathway but as a dynamic network incorporating multiple feedback loops, thereby constituting a critical threshold mechanism that determines the success of decidualization. Disruption of this axis provides a shared mechanistic basis for pathologies such as recurrent implantation failure, pregnancy loss, and preeclampsia. From a therapeutic perspective, agents including chemical chaperones, UPR modulators, AMPK activators, and anti-inflammatory compounds hold translational potential by targeting these pathological feedback circuits. However, key knowledge gaps remain, particularly regarding the cell type-specific and temporal regulation of ER stress, the molecular boundaries defining the transition from adaptive to pathological states, and interspecies differences. Future studies employing single-cell omics approaches and functional in vivo models will be essential to elucidate the dynamic organization of this axis and to enable the development of targeted and personalized therapeutic strategies. Full article

Prostate cancer (PCa) progression and treatment resistance are driven by tumor-intrinsic mechanisms and adaptive remodeling of the tumor microenvironment, in which cancer-associated fibroblasts (CAFs) play a crucial role. Although CAF biology is increasingly recognized, a major translational gap remains: CAFs are highly heterogeneous, and comprise distinct functional states with divergent effects on disease progression, immune regulation, and therapeutic resistance. To bridge this gap, we synthesize evidence from single-cell and spatial transcriptomic studies, tissue-based pathology, liquid biopsy assays, and molecular imaging to construct an evidence-tiered, decision-oriented translational framework that connects stromal mechanisms, translational measurement strategies, and therapeutic interventions in PCa. Single-cell and spatial transcriptomic analyses have consistently identified multiple CAF programs, including matrix-remodeling, inflammatory, immunoregulatory, antigen-presenting, and therapy-imprinted states, each with distinct functional outputs and clinical correlates. Tissue-based readouts, including reactive stromal grade (RSG) and fibroblast activation protein (FAP) immunohistochemistry, provide practical proxies for stromal activation and correlate with disease-specific mortality and imaging phenotypes. Circulating CAFs (cCAFs) represent an emerging liquid biopsy modality for longitudinal stromal monitoring, although technical standardization is required before clinical implementation. FAP-targeted PET imaging and emerging dual prostate-specific membrane antigen (PSMA)/FAP-targeted theranostic strategies provide noninvasive tools for patient selection and response assessment, particularly in PSMA-discordant or tracer-heterogeneous disease. Androgen receptor (AR)-targeted therapy can reprogram stromal states toward resistance-promoting circuits, highlighting the dynamic and plastic nature of the CAF compartment. A state-based CAF framework organizes stromal biology into testable translational hypotheses rather than immediate clinical standards. RSG and FAP-based tissue or imaging readouts are practical markers of stromal activation, whereas spatial CAF-immune signatures and cCAF assays remain investigational and require assay harmonization and prospective validation. Future trials should pre-specify stromal biomarkers as enrichment or pharmacodynamic variables when matched to the intervention and should avoid treating CAFs as a uniform therapeutic target. Full article

Biomechanics sits at the interface of engineering and medical sciences, offering essential insight into how tissues, organs, and biological systems respond to mechanical loading. This review brings together recent advances in musculoskeletal and cardiovascular biomechanics, illustrating how experimental techniques, computational modeling, and multiscale analysis are used to characterize load transfer, tissue deformation, fatigue, and injury mechanisms. In musculoskeletal applications, predictive simulations, wearable sensing technologies, and neuromechanical assessment tools support improved injury prevention, rehabilitation planning, and assistive device development. In the cardiovascular domain, patient-specific modeling, fluid–structure interaction analyses, and advanced imaging approaches clarify how hemodynamics, vessel wall mechanics, and device–tissue interactions influence disease progression, implant performance, and therapeutic outcomes. Emerging technologies including artificial intelligence, machine learning, digital twin frameworks, biofabrication, soft robotics, and self-powered sensing are enabling data-driven, real-time, and personalized interventions that connect mechanistic understanding with clinical practice. Despite these advances, challenges remain in accounting for individual variability, integrating multiscale data, and translating computational predictions into clinically validated solutions. By emphasizing interdisciplinary strategies that unite biomechanics, computational analytics, and innovative device engineering, this review outlines a pathway toward predictive, patient-centered healthcare and next-generation therapeutic and rehabilitation solutions. Full article