Search Results (10,708)

Background: Diabetes mellitus (DM) and chronic kidney disease (CKD) are major contributors to global morbidity and mortality, with disease progression being closely linked to persistent inflammation, oxidative damage, and apoptotic pathways. Icariin (ICA), a bioactive flavonoid compound isolated from Epimedium brevicornum Maxim, has attracted considerable interest because of its diverse pharmacological properties. We evaluated the effect of ICA on streptozotocin (STZ)-induced diabetic rats with or without adenine-induced CKD. This combined model reproduces several key structural and functional characteristics observed in human diabetic kidney disease and advanced CKD. Methods: Male Wistar rats were allocated to five treatment groups and followed for 35 days. Group 1 served as the untreated control and received standard chow; Group 2 was administered streptozotocin (STZ); Group 3 received STZ together with icariin (ICA); Group 4 received a combination of adenine and STZ; and Group 5 was treated with adenine, STZ, and ICA. ICA was administered at a dose of 200 mg/kg by oral gavage. Biochemical, oxidative stress and inflammatory markers were assessed. Results: Rats treated with STZ, with or without adenine, exhibited significant hyperglycemia, elevated plasma levels of cystatin C and indoxyl sulphate, increased urinary levels of N-acetyl-β-D-glucosaminidase (NAG) and NAG/creatinine ratio, and reduced creatinine clearance. Additionally, there were significant decreases in renalase activity and urine osmolality, significant increases in interleukins IL-1β and IL-6 and TNF-alpha levels, and a decrease in IL-10 level. Oxidative stress biomarkers were also significantly impaired in both groups, along with significant renal histopathological changes. ICA significantly ameliorated these alterations in both experimental groups. Conclusion: These findings demonstrate that ICA exerts renoprotective and anti-inflammatory effects in a clinically relevant model of advanced diabetic CKD. Further studies are warranted to elucidate the underlying mechanisms and determine the translational relevance of these findings. Full article

The increasing prevalence of antimicrobial resistance (AMR) has promoted the development of advanced biomaterials capable of overcoming the limitations of conventional antibiotics. In this context, metal nanoparticle hybrid hydrogels (MNHHs) have emerged as multifunctional platforms that integrate the high water-retention capacity and biocompatibility of hydrogels with the antimicrobial properties of metallic nanoparticles (MNPs). This review critically analyzes recent advances in the design, physicochemical properties, antimicrobial mechanisms, and biomedical applications of these systems. Current evidence demonstrates that MNHHs can achieve antimicrobial efficiencies above 98–99%, with minimum inhibitory concentrations as low as 0.78 µg mL⁻¹ and inhibition zones of up to 25 mm against clinically relevant pathogens. Furthermore, the incorporation of MNPs significantly improves the mechanical properties of hydrogels and enables controlled and sustained metal ion release for periods of up to 14 days. Despite these promising results, important challenges remain regarding cytotoxicity, release control, the lack of experimental standardization, and the limited understanding of long-term biological effects. Overall, MNHHs represent a promising strategy for infection control, regenerative medicine, and controlled drug delivery; however, their clinical translation still requires the development of reproducible, safe, scalable, and highly biocompatible systems. Full article

Background: Glioblastoma (GBM) is the most aggressive primary brain tumor in adults and remains associated with poor prognosis despite multimodal treatment. Ferroptosis, an iron-dependent form of regulated cell death driven by lipid peroxidation and redox imbalance, has recently emerged as a potential therapeutic vulnerability in glioma. This review summarizes current knowledge on the molecular regulation of ferroptosis in glioma and discusses its implications for tumor progression, therapeutic resistance, and translational targeting. Methods: A structured narrative review of the literature was conducted using PubMed/MEDLINE, Scopus, and Web of Science databases. Experimental, translational, and clinically relevant studies investigating ferroptosis-related mechanisms and therapeutic strategies in glioma and GBM were qualitatively analyzed. Results: Ferroptosis in glioma is regulated by interconnected pathways involving iron metabolism, phospholipid remodeling, oxidative stress, and antioxidant defense systems, particularly the SLC7A11–glutathione–GPX4 axis. Additional protective mechanisms mediated by FSP1 and DHODH, together with regulatory networks involving NRF2, ATF4, p53, and hypoxia-related signaling, contribute to adaptive resistance to ferroptosis. Increasing evidence indicates that ferroptosis interacts bidirectionally with the glioma tumor microenvironment and may exert both antitumor and immunosuppressive effects. Preclinical studies further suggest that ferroptosis induction may enhance the efficacy of temozolomide, radiotherapy, and immunotherapy, although clinical translation remains limited by tumor heterogeneity, blood–brain barrier penetration, and resistance mechanisms. Conclusions: Ferroptosis represents a biologically plausible and therapeutically promising target in glioma. Improved understanding of ferroptosis regulation, tumor microenvironment interactions, and biomarker-guided therapeutic strategies may support the future development of more effective treatments for GBM. Full article

Background/Objectives: The “small-n-large-p” dilemma in mammography artificial intelligence (AI)—where the number of candidate imaging features far exceeds the number of labeled cases—commonly results in model overfitting, unstable feature selection, and poor generalization across clinical settings. This study aims to reassess the internal performance of the HiTopology-GOA-CSA (Grasshopper Optimization Algorithm–Crow Search Algorithm) feature-selection framework for mammography using a larger real Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) cohort and a stricter leakage-aware evaluation strategy. Methods: In this retrospective computational study using public anonymized datasets, an expanded internal cohort of 98 CBIS-DDSM mass cases (49 benign, 49 malignant) was assembled from digital imaging and communications in medicine (DICOM) region of interest (ROI) series. A total of 1074 features were extracted per case, including 88 handcrafted radiomic descriptors and 986 EfficientNet-B5 deep features. HiTopology-GOA-CSA selected 102 features, corresponding to 91% feature reduction. Two internal evaluation modes were compared: Mode A, which matched the original pilot methodology by performing feature selection once on the full cohort before cross-validation, and Mode B, which used strict nested feature selection within training folds. Performance was assessed with 5-fold stratified cross-validation using a multilayer perceptron (MLP) classifier. Results: On the expanded cohort, Mode A achieved an area under the receiver operating characteristic curve (AUC) of 0.726 (95% CI: 0.594–0.858), sensitivity of 0.658, specificity of 0.651, and F1-score of 0.644. Under the stricter nested evaluation, Mode B achieved AUC of 0.683 (95% CI: 0.549–0.817), sensitivity of 0.598, specificity of 0.631, and F1-score of 0.595. Mean pairwise Jaccard similarity across nested folds was 0.604, indicating moderate feature stability. Benchmark comparisons showed that the proposed method was competitive but did not outperform standard baselines; LASSO logistic regression achieved the highest AUC of 0.739, while the proposed HiTopology-GOA-CSA + MLP achieved an AUC of 0.683. Real external validation on the locked VinDr-Mammo subset (n = 25) remained near-random (AUC of 0.500 [95% CI: 0.304–0.696]), with complete prediction collapse (sensitivity of 1.000, specificity of 0.000). Conclusions: The framework demonstrated feasibility for structured feature selection and stress testing in a small-cohort mammography AI setting; however, external validation revealed near-random discrimination and prediction collapse, indicating limited generalizability. These findings emphasize the need for benchmark comparisons, transparent uncertainty reporting, patient-level validation, and larger multicenter datasets before clinical translation. Full article

Background/Objectives: Survival prediction in non-small cell lung cancer (NSCLC) remains challenging, particularly in limited-sample settings where end-to-end deep learning models may suffer from limited generalization. This study aimed to develop a data-efficient, multimodal, and explainable framework integrating computed tomography (CT)-derived imaging information with clinical variables for NSCLC survival prediction. Methods: CT images, tumor segmentations, and clinical data from the publicly available NSCLC Radiomics (LUNG1) dataset (377 patients) were used. Tumor-focused regions were extracted using segmentation masks, and pretrained RadImageNet-InceptionV3 embeddings were obtained from the largest tumor-containing slice and neighboring-slice summaries. Deep imaging embeddings, engineered imaging features, and clinical variables were fused into a unified tabular representation. To improve robustness under limited-sample conditions, feature blocks were compressed using principal component analysis. CatBoost, XGBoost, and LightGBM models were trained on a development set and evaluated on a strictly held-out final validation set. Results: In three-class survival stratification, assigning censored/non-event patients to the upper survival group produced the strongest ordinal prognostic performance. Under the EX_PLUS_NON_EX_TOP setting, CatBoost achieved the best holdout score-based class C-index of 0.655. In continuous survival regression, LightGBM achieved the best holdout event-patient C-index of 0.576. Clinical variables provided the dominant prognostic signal, while compact deep image embeddings contributed complementary information, particularly in separating short- and long-survival groups. SHAP analysis confirmed contributions from both clinical and image-derived features. Conclusions: The proposed framework provides a proof-of-concept demonstration of a data-efficient and explainable image-to-tabular approach for NSCLC survival prediction under strict internal holdout validation. The results suggest that pretrained CT embeddings, clinical variables, gradient-boosted trees, and SHAP-based interpretation can be combined in a feasible, limited-sample survival modeling pipeline, while external validation remains necessary before clinical translation. Full article

Head and neck cancer (HNC), particularly oral squamous cell carcinoma (OSCC), remains a major clinical challenge due to its aggressive behavior, high recurrence rates, and frequent resistance to conventional therapies. Natural compounds, especially curcumin and its derivatives, have gained increasing attention as potential anticancer agents due to their ability to target multiple molecular pathways involved in tumor progression. This review critically evaluates the current preclinical and translational evidence supporting curcumin and its derivatives as monotherapeutic agents in HNC, with particular emphasis on oral cancer. We integrate the available evidence to assess the biological rationale, therapeutic potential, and current limitations of curcumin-based approaches. The molecular mechanisms underlying their antitumor activity are discussed, including modulation of EGFR/ERK and PI3K/Akt signaling pathways, inhibition of NF-κB and STAT3 activation, induction of apoptosis, regulation of oxidative stress, and suppression of epithelial–mesenchymal transition and tumor invasiveness. In addition, we address the impact of curcumin on the tumor microenvironment and its role in overcoming intrinsic cellular resistance mechanisms. The review also highlights advances in drug delivery strategies, such as nanoformulations, that are designed to improve curcumin’s bioavailability and therapeutic efficacy. By critically integrating current evidence, this review highlights both the promise and the challenges associated with curcumin-based monotherapy in HNC, emphasizing the need for more robust and clinically relevant studies to support future translation. Full article

Young barley, derived from the early vegetative stage of Hordeum vulgare L., constitutes a plant-based functional ingredient whose phytochemical profile differs markedly from that of mature grain. Two principal commercial forms exist—dried grass powder and juice-derived products—differing in matrix composition and bioactive compound concentration. This narrative review critically evaluates the current knowledge on the phytochemical composition, biological activity, and translational relevance of young barley preparations considered as a functional plant food. The phytochemical spectrum is dominated by C-glycosyl flavones, particularly saponarin and lutonarin, alongside phenolic acids, chlorophylls, enzymatic antioxidants, vitamins, and minerals. Experimental evidence implicates the modulation of redox homeostasis, inflammatory signaling, and metabolic regulators as the primary biological mechanisms. In vitro studies additionally demonstrate antiproliferative activity in human cancer cell lines and immunomodulatory properties mediated by polysaccharide-rich fractions, extending the biological profile of young barley beyond classical antioxidant activity. Although preclinical models consistently demonstrate antioxidant and metabolic effects, high experimental doses and limited preparation standardization restrict the direct extrapolation to human supplementation contexts. Available clinical trials suggest modest improvements in selected lipid, glycemic, and oxidative stress markers; yet, most are small in scale and brief in duration. Agronomic variables including fertilization strategy and soil composition represent additional, underappreciated sources of phytochemical variability and safety concern. Overall, the current evidence supports the biological plausibility of young barley as a functional plant food; yet, the clinical data remain preliminary. Future research should prioritize preparation standardization, dose–response characterization, and agronomic transparency to strengthen translational reliability. In conclusion, young barley preparations represent a biologically plausible functional plant food ingredient with preliminary clinical support, pending confirmation from adequately powered, standardised randomised controlled trials. Full article

Artificial intelligence (AI) is increasingly influencing nanopharmaceutical development by supporting the transition from empirical formulation screening toward predictive, data-driven, and translationally oriented design. Nanocarrier-based therapeutics are governed by nonlinear relationships among material composition, physicochemical attributes, manufacturing parameters, biological identity, pharmacokinetics, toxicity, and therapeutic performance. In this review, we examine how AI can contribute to nanopharmaceutical development from predictive formulation design to clinical translation. We synthesize current applications of machine learning, deep learning, physics-informed modeling, hybrid mechanistic–AI approaches, and automated optimization workflows, with emphasis on critical quality attribute modeling, multi-objective optimization, design of experiments, quality-by-design, process analytical technology, digital twins, and continuous manufacturing. We also discuss applications involving nano–bio interactions, pharmacokinetics, toxicity, immunogenicity, and precision nanomedicine. AI-based approaches can support rational nanocarrier design, identify nonlinear formulation–property relationships, guide optimization, improve process understanding, and integrate heterogeneous experimental, biological, and manufacturing datasets across diverse nanopharmaceutical platforms. These methods are particularly relevant for modeling protein corona formation, cellular uptake, intracellular trafficking, biodistribution, pharmacokinetics, toxicity, immunogenicity, and patient-specific responses. However, translational implementation remains limited by fragmented datasets, inconsistent reporting standards, limited interpretability, insufficient external validation, uncertain predictions, poorly defined applicability domains, and evolving regulatory expectations for adaptive computational models. Overall, AI should be viewed not only as an optimization tool, but also as a translational framework connecting formulation science, biological prediction, manufacturing control, and clinical implementation. Future progress will depend on standardized data infrastructures, explainable and externally validated models, uncertainty quantification, applicability-domain definition, hybrid mechanistic–AI frameworks, regulatory-ready documentation, and clinically relevant case studies. Full article

This bibliometric study examines the intellectual structure, evolution, and collaboration patterns of phytotherapy research within sports science to identify key themes and research gaps. Publications indexed in the Web of Science Core Collection from 1991 to 2024 were analyzed using a search strategy combining phytotherapy and sports medicine terms, yielding 3404 records, of which 368 met the inclusion criteria after systematic screening. Performance analysis assessed publication trends, citation impact, and author productivity, while science mapping techniques—including keyword co-occurrence, bibliographic coupling, and co-authorship network analysis—were conducted using Bibliometrix and VOSviewer. Thematic positioning was evaluated through Callon’s centrality-density framework. Results indicate steady growth in the field, with a CAGR of 11.83% and peak output in 2021, involving 2103 authors across 199 sources. International collaboration reached 22.55%, led by the United States, United Kingdom, and China. Dominant research themes include exercise, inflammation, oxidative stress, and phytochemicals such as curcumin and resveratrol. Thematic mapping highlights exercise performance and supplementation as central topics. Overall, the field demonstrates significant expansion, though increased international collaboration and clinical translation are needed. Full article

Medical infrared thermography, which involves the use of infrared thermal cameras for the non-invasive assessment of skin surface temperature distribution, has gained increasing interest in recent years as a tool supporting diagnosis and treatment monitoring. The aim of this article is to present the historical background and critically reassess the current role of infrared thermography in medicine, with particular emphasis on standardization as a key determinant of its clinical utility. This Perspective highlights the fundamental impact of methodological variability on diagnostic performance and reproducibility. A structured framework for standardization is proposed, encompassing patient preparation, environmental conditions, device parameters and calibration, image acquisition protocols, region-of-interest definition and analysis, as well as reporting and clinical interpretation. The analysis demonstrates how inconsistencies at each of these levels reduce measurement reliability, limit inter-study comparability, and weaken clinical confidence in infrared thermography. The article also addresses the growing availability of mobile thermal imaging systems and their integration with artificial intelligence, while emphasizing the need for stronger evidence-based support across all methodological domains. The presented analysis suggests that, despite existing limitations, medical infrared thermography holds considerable potential as a supportive clinical tool. However, its broader clinical implementation remains limited by several factors, with the lack of standardized protocols constituting a major and practically addressable translational barrier. Wider adoption will require standardization efforts alongside rigorous validation studies and application-specific interpretative guidelines. Addressing these challenges through technological advances and coordinated international standardization may facilitate meaningful progress over the next decade. Full article

Persistent high-risk human papillomavirus infection is a critical prerequisite for cervical intraepithelial neoplasia and cervical cancer, yet viral factors alone do not fully explain why most infections clear while a subset persists and progresses. Emerging longitudinal, multi-omics, and mechanistic evidence supports a plausible model in which the cervicovaginal microbiota is not a passive bystander but a functional determinant of mucosal immunity, epithelial barrier integrity, and local metabolic tone. Lactobacillus-dominant community states, particularly those enriched in Lactobacillus crispatus, are generally associated with lower pH, regulated inflammatory signaling, stronger barrier function, and a higher likelihood of HPV clearance. In contrast, anaerobe-enriched dysbiosis is linked to elevated pro-inflammatory cytokines, altered antigen presentation, immune checkpoint signatures consistent with T-cell dysfunction, and metabolic shifts involving lactate depletion and accumulation of short-chain fatty acids and other metabolites that can influence epithelial and immune-cell programs. Importantly, the interaction is bidirectional: hrHPV can remodel the microenvironment by suppressing host defense peptides and perturbing mucosal barriers, thereby reducing Lactobacillus fitness and reinforcing dysbiosis in a feed-forward loop that favors persistence and oncogenic progression. This review integrates functional ecology, longitudinal clinical evidence, immunological and metabolic mechanisms, and translational implications, highlighting opportunities for microbiome-informed risk stratification and adjunctive interventions, as well as key gaps requiring standardized longitudinal multi-omics and rigorously designed clinical trials. Full article

Usnic acid (UA), a prominent lichen secondary metabolite, exhibits a unique dual therapeutic profile in dermatology, though its clinical translation is limited by systemic hepatotoxicity and poor solubility. This review comprehensively evaluates the topical efficacy, molecular mechanisms, and advanced formulation strategies of UA enantiomers and UA-rich extracts. A literature search across PubMed, Scopus, and Google Scholar identified 36 original publications focusing on anti-melanoma activity, photoprotection, and tissue regeneration. In vitro studies demonstrate that UA induces apoptosis in resistant melanoma cell lines (A375, HTB-140) via extrinsic/intrinsic pathways, with (−)-UA effectively overcoming doxorubicin resistance. Conversely, in non-cancerous models, low concentrations of UA accelerate wound and burn healing by upregulating vascular endothelial growth factor (VEGF), stimulating fibroblast proliferation, and optimizing extracellular matrix remodeling while preventing hypertrophic scarring. To mitigate skin sensitization and systemic risks, advanced drug delivery systems—including liposomes, nanoemulsions, chitosan nanogels, and electrospun scaffolds—have been developed, significantly enhancing skin permeability and localized dermal retention. Ultimately, the development of bio-functionalized smart dressings and targeted nano-formulations represents the most viable path toward unlocking the full clinical potential of UA in modern dermatological and oncological care. Full article

Edible insects are increasingly recognized as a viable alternative protein source, offering a potentially sustainable approach to addressing global food security challenges. This narrative review critically examines the nutritional composition, environmental advantages, techno-functional attributes, and potential applications of insect-based proteins within human food systems. Edible insects are characterized by high protein content, favourable essential amino acid profiles, and appreciable levels of key micronutrients, rendering them nutritionally comparable to conventional livestock-derived proteins. Moreover, insect production systems generally require substantially lower inputs of land, water, and feed, resulting in comparatively lower greenhouse gas emissions and reduced overall environmental burden. Despite these advantages, broader adoption remains constrained by challenges related to regulatory heterogeneity, food safety concerns, and limited consumer acceptance. Overall, the available evidence suggests that edible insects can function as a nutritionally adequate and environmentally sustainable complementary protein source; however, significant variability in nutrient composition, limitations in standardized safety assessment, and socio-cultural barriers currently restrict their large-scale integration into mainstream food systems. In addition, inconsistencies in analytical methodologies and reliance on in vitro data further complicate cross-study comparisons and translational relevance. Future research should focus on standardization of rearing and processing conditions, harmonization of evaluation frameworks (e.g., protein quality indices), comprehensive safety assessments, and well-designed clinical studies to validate nutritional and functional benefits, alongside the development of effective strategies to improve consumer acceptance and support regulatory alignment across regions. Full article

Antisense oligonucleotides (ASOs) are a class of nucleic acid therapeutics that modulate gene expression through diverse mechanisms. Since their initial demonstration in inhibiting viral genes, advances in medicinal chemistry, pharmacology, and delivery have enabled robust and durable target engagement across multiple tissues. Chemical modifications to the backbone, ribose, and nucleobases have improved nuclease resistance, binding affinity, and pharmacokinetics, while conjugation and delivery technologies have expanded tissue accessibility. Beyond classical RNase H–mediated RNA degradation, ASOs regulate gene expression via splicing modulation, microRNA inhibition, transcriptional activation, and translation modulation, supporting both gene silencing and upregulation strategies. Multiple ASO drugs are now approved, particularly for genetic diseases, with many more in clinical development. This review outlines the evolution of antisense technology, key chemical and delivery innovations, ASO pharmacokinetics and intracellular trafficking, the mechanisms underlying gene regulation, and current clinical applications and future opportunities. Full article

Prostate cancer remains a major therapeutic challenge, particularly after progression to castration-resistant disease, where persistent androgen receptor signaling, metabolic adaptation, immune escape, and treatment resistance jointly limit clinical benefit. Regulated cell death (RCD) is increasingly recognized not only as an endpoint of tumor cell elimination but also as a dynamic regulator of prostate cancer progression, therapeutic vulnerability, and tumor–immune interactions. In this review, we propose an immunometabolic framework in which androgen receptor signaling, lipid and redox metabolic reprogramming, oxidative stress, and therapeutic pressure converge to shape the susceptibility of prostate cancer cells to distinct RCD modalities. We focus on autophagy and ferroptosis as two extensively studied and translationally relevant pathways, while also discussing emerging roles of necroptosis, pyroptosis, and cuproptosis. Particular attention is given to how RCD-associated signals, including damage-associated molecular patterns, inflammatory mediators, and lipid peroxidation products, may remodel the tumor immune microenvironment and influence the transition between immune-cold and immune-inflamed phenotypes. We further summarize RCD-targeted therapeutic strategies, including ferroptosis induction, autophagy inhibition, nanodrug delivery systems, rational combination therapy, and biomarker-guided patient stratification. Finally, we discuss key translational barriers, including context-dependent biological effects, limited clinical validation, tumor heterogeneity, adaptive resistance, and insufficient predictive biomarkers. By integrating cell death biology with metabolic reprogramming, immune remodeling, and therapeutic resistance, this review highlights RCD as a promising but context-dependent therapeutic vulnerability in advanced prostate cancer. Full article