Search Results (571)

(1) Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) is characterized by liver damage similar to alcoholic fatty liver disease, including triglyceride infiltration of hepatocytes, regardless of alcohol consumption. It leads to progressive liver damage, such as loss of liver function, cirrhosis, and liver cancer, and the response rate of drugs under clinical research is less than 50%. There is an urgent need for biomarkers to evaluate the efficacy of these drugs. (2) Methods: MASLD was induced in mice using a High-Fat diet (HF), Western diet (WD), and Methionine/Choline-Deficient diet (MCD) for 20 weeks (4 weeks for MCD). Liver tissue biopsies were performed, and the treatment effects of saponin and non-saponin feeds were evaluated. Fat accumulation and hepatic inflammation were measured, and mRNA sequencing analysis was conducted. The therapeutic effects were validated using patient-derived liver organoids. (3) Results: The NAFLD Activity Score (NAS) significantly increased in all MASLD models compared with controls. Saponin treatment decreased NAS in the HF and WD groups but not in the MCD group. RNA sequencing and PCA analysis showed that the HF saponin response samples were similar to normal controls. DAVID analysis revealed significant changes in lipid, triglyceride, and fatty acid metabolic processes. qRT-PCR confirmed decreased fibrosis markers in the HF saponin response group, and GSEA analysis showed reduced HAMP1 gene expression. (4) Conclusions: Among the diets, red ginseng was most effective in the HF diet, with significant effects in the saponin-treated group. The therapeutic efficacy was better when HAMP1 expression was increased. Therefore, we propose HAMP1 as a potential exploratory biomarker to assess the saponin response in a preclinical setting. In addition, the reduction of inflammation and hepatic iron accumulation suggests that saponins may exert antioxidant effects through modulation of oxidative stress. Full article

Alzheimer’s Disease and Dementia (ADD) progresses along a continuum of cognitive decline, typically from Subjective Cognitive Impairment (SCI) to Mild Cognitive Impairment (MCI) and eventually to dementia. While many studies have focused on classifying these clinical stages, fewer have examined whether brain connectomes encode this continuum in a low-dimensional, interpretable form. Motivated by the hypothesis that structural brain connectomes undergo complex yet compact changes across cognitive decline, we propose a Graph Neural Network (GNN)-based framework that embeds these connectomes into a two-dimensional manifold to capture the evolving patterns of structural connectivity associated with cognitive deterioration. Using attention-based graph aggregation and Principal Component Analysis (PCA), we find that MCI subjects consistently occupy an intermediate position between SCI and ADD, and that the observed transitions align with known clinical biomarkers of ADD pathology. This hypothesis-driven analysis is further supported by the model’s robust separation performance, with ROC-AUC scores of 0.93 for ADD vs. SCI and 0.81 for ADD vs. MCI. These findings offer an interpretable and neurologically grounded representation of dementia progression, emphasizing structural connectome alterations as potential markers of cognitive decline. Full article

Cardiac fibrosis following myocardial infarction plays a critical role in the formation of scar tissue and contributes to ventricular arrhythmias, including ventricular tachycardia and sudden cardiac death. Current clinical diagnostics use electrical and structural markers, but lack precision due to low spatial resolution and absence of molecular information. In this paper, we employed line scan Raman microspectroscopy to classify sheep myocardial tissue into muscle, necrotic, granulated, and fibrotic tissue types, using collagen as a molecular biomarker. Three spectral regions were evaluated: region A (600–2960 cm⁻¹), region B (600–1399 cm⁻¹ and 1751–2960 cm⁻¹), and region C (1400–1750 cm⁻¹), which includes the prominent collagen-associated peaks at 1448 cm⁻¹ and 1652 cm⁻¹. Linear and nonlinear principal component analysis (PCA) and support vector machines (SVMs) were applied for dimensionality reduction and classification, with nonlinear models specifically addressing the nonlinearity of collagen formation during fibrogenesis. Histological validation was performed using Masson’s trichrome staining. Raman bands associated with collagen in region C consistently outperformed regions A and B, achieving the highest explained variance and best class separation in both binary and multiclass PCA models for both linear and nonlinear approaches. The ratio of collagen-related peaks enabled stage-dependent tissue characterization, confirming the nonlinear nature of fibrotic remodeling. Our findings highlight the diagnostic potential of collagen-associated Raman bands for characterizing myocardial fibrosis. The proposed PCA-SVM framework demonstrates robust performance even with limited sample size and has the potential to lay the foundation for real-time intraoperative diagnostics. Full article

Background/Objectives: Prostate cancer (PCa) remains a major global health issue, associated with significant mortality and morbidity. Despite advances in diagnosis and treatment, predicting biochemical recurrence (BCR) after radical prostatectomy remains challenging, highlighting the need for reliable biomarkers to guide prognosis and therapy. The study aimed to evaluate the prognostic significance of the PTEN and ERG biomarkers in predicting BCR and tumor progression in PCa patients who underwent radical prostatectomy. Methods: This study consisted of a cohort of 91 patients with localized PCa who underwent radical prostatectomy between 2016 and 2022. From this cohort, 77 patients were selected for final analysis. Tissue microarrays (TMAs) were constructed from paraffin blocks, and immunohistochemical (IHC) staining for PTEN and ERG was performed using specific antibodies on the Ventana BenchMark ULTRA system (Roche Diagnostics, Indianapolis, IN, USA). Stained sections were evaluated and correlated with clinical and pathological data. Results: PTEN expression showed a significant negative correlation with BCR (r = −0.301, p = 0.014), indicating that reduced PTEN expression is associated with increased recurrence risk. PTEN was not significantly linked to PSA levels, tumor stage, or lymph node involvement. ERG expression correlated positively with advanced pathological tumor stage (r = 0.315, p = 0.005) but was not associated with BCR or other clinical parameters. Conclusions: PTEN appears to be a valuable prognostic marker for recurrence in PCa, while ERG may indicate tumor progression. These findings support the potential integration of PTEN and ERG into clinical practice to enhance risk stratification and personalized treatment, warranting further validation in larger patient cohorts. Full article

Milk is a nutritionally rich food and a frequent target of economically motivated adulteration, particularly through substitution with lower-cost milk types. Over the past decade, significant progress has been made in the authentication of milk using advanced proteomic and chemometric approaches, with a focus on the discovery and application of protein and peptide biomarkers for species differentiation and fraud detection. Recent innovations in both top-down and bottom-up proteomics have markedly improved the sensitivity and specificity of detecting key molecular targets, including caseins and whey proteins. Peptide-based methods are especially valuable in processed dairy products due to their thermal stability and resilience to harsh treatment, although their species specificity may be limited when sequences are conserved across related species. Robust chemometric approaches are increasingly integrated with proteomic pipelines to handle high-dimensional datasets and enhance classification performance. Multivariate techniques, such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), are frequently employed to extract discriminatory features and model adulteration scenarios. Despite these advances, key challenges persist, including the lack of standardized protocols, variability in sample preparation, and the need for broader validation across breeds, geographies, and production systems. Future progress will depend on the convergence of high-resolution proteomics with multi-omics integration, structured data fusion, and machine learning frameworks, enabling scalable, specific, and robust solutions for milk authentication in increasingly complex food systems. Full article

Tomatoes are globally esteemed not only for their nutritional value but also for their complex and appealing aroma, a key determinant of consumer preference. The present study aimed to comprehensively characterise the volatilomic fingerprints of three tomato species—Solanum lycopersicum L., S. lycopersicum var. cerasiforme, and S. betaceum—encompassing six distinct varieties, through the application of headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME/GC-MS). A total of 55 volatile organic compounds (VOCs) spanning multiple chemical classes were identified, of which only 28 were ubiquitously present across all varieties examined. Carbonyl compounds constituted the predominant chemical family, with hexanal and (E)-2-hexenal emerging as putative key contributors to the characteristic green and fresh olfactory notes. Notably, esters were found to dominate the unique volatile fingerprint of cherry tomatoes, particularly methyl 2-hydroxybenzoate, while Kumato and Roma varieties exhibited elevated levels of furanic compounds. Multivariate statistical analyses, including principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), demonstrated clear varietal discrimination and identified potential aroma-associated biomarkers such as phenylethyl alcohol, 3-methyl-1-butanol, hexanal, (E)-2-octenal, (E)-2-nonenal, and heptanal. Collectively, these findings underscore the utility of volatilomic fingerprint as a robust tool for varietal identification and quality control within the food industry. Full article

Out of 2495 samples, L. monocytogenes was isolated from 262 (10.5%). Among these, 30 isolates (11.5% of the 262) exhibited unique phenotypic and genetic characteristics compared to reference strains. Hemolysin-negative L. monocytogenes isolates have been increasingly reported in recent years and are challenging to identify due to their altered phenotypic traits and limitations of standard microbiological methods. This study aimed to evaluate the performance of MALDI-TOF MS in identifying and differentiating 30 hemolysin-negative and hemolysin-positive L. monocytogenes isolates and 12 reference strains, using both a commercial Bruker database and a proprietary in-house database developed from newly characterized isolates. The Bruker database correctly identified only 21% of the environmental isolates, misclassifying most as L. innocua, and showed 83.3% accuracy for reference strains. In contrast, the in-house database achieved 96.6% and 100% accuracy for the environmental and reference strains, respectively. Statistical methods, including hierarchical clustering, heatmaps, PCA, and Pearson correlation, revealed grouping based on phenotypic traits and origin, with key peptides influencing classification. Biomarkers linked to hemolysis and antibiotic resistance differentiated the environmental isolates from reference strains. These findings highlight the need for the development of customized spectral databases to improve the detection of L. monocytogenes in food safety monitoring. Full article

Urinary extracellular vesicles (U-EVs) are gaining increasing interest as non-invasive liquid biopsy tools for clinical use. Prostate cancer (PCa) is amongst the highest cancer-related cause of death in men, and therefore, the identification of non-invasive robust biomarkers is of high importance. This study assessed U-EV profiles from individuals affected by PCa at Gleason scores 6–9, compared with healthy controls. U-EVs were characterised and assessed for proteomic cargo content by LC-MS/MS analysis. The U-EV proteomes were compared for enrichment of gene ontology (GO), KEGG, and Reactome pathways, as well as disease–gene associations. U-EVs ranged in size from 50 to 350 nm, with the majority falling within the 100–200 nm size range for all groups. U-EV protein cargoes from the PCa groups differed significantly from healthy controls, with 16 protein hits unique to the GS 6–7 and 88 hits to the GS 8–9 U-EVs. Pathway analysis showed increased enrichment in the PCa U-EVs of biological process GO (5 and 37 unique to GS 6–7 and GS 8–9, respectively), molecular function GO (3 and 6 unique to GS 6–7 and GS 8–9, respectively), and cellular component GO (10 and 22 unique to GS 6–7 and GS 8–9, respectively) pathways. A similar increase was seen for KEGG pathways (11 unique to GS 8–9) and Reactome pathways (102 unique to GS 8–9). Enrichment of disease–gene associations was also increased in the PCa U-EVs, with highest differences for the GS 8–9 U-EVs (26 unique terms). The pathway enrichment in the PCa U-EVs was related to several key inflammatory, cell differentiation, cell adhesion, oestrogen signalling, and infection pathways. Unique GO and KEGG pathways enriched for the GS 8–9 U-EVs were associated with cell–cell communication, immune and stress responses, apoptosis, peptidase activity, antioxidant activity, platelet aggregation, mitosis, proteasome, mRNA stability oxytocin signalling, cardiomyopathy, and several neurodegenerative diseases. Our findings highlight U-EVs as biomarkers to inform disease pathways in prostate cancer patients and offer a non-invasive biomarker tool for clinical use. Full article

Artificial insemination (AI) is a key breeding technique in the swine industry; however, the lack of reliable biomarkers for semen quality limits its effectiveness. Seminal plasma (SP) contains extracellular vesicles (EVs) that present a promising, non-invasive biomarker for semen quality. This study explores the biochemical profiles of boar SP to assess semen quality through near-infrared spectroscopy (NIRS) and proteomics of SP-EVs. Fresh semen from mature Duroc boars was evaluated based on sperm motility, classifying samples as Passed (≥70%) or Failed (<70%). NIRS analysis identified distinct variations in water structures at specific wavelengths (C1, C5, C12 nm), achieving high accuracy (92.2%), sensitivity (94.2%), and specificity (90.3%) through PCA-LDA. Proteomic analysis of SP-EVs revealed 218 proteins in Passed and 238 in Failed samples. Nexin-1 and seminal plasma protein pB1 were upregulated in Passed samples, while LGALS3BP was downregulated. The functional analysis highlighted pathways associated with single fertilization, filament organization, and glutathione metabolism in Passed samples. Integrating NIRS with SP-EV proteomics provides a robust approach to non-invasive assessment of semen quality. These findings suggest that SP-EVs could serve as effective biosensors for rapid semen quality assessment, enabling better boar semen selection and enhancing AI practices in swine breeding. Full article

In clinical applications of surface-enhanced Raman spectroscopy (SERS) immunosensors, accurately determining analyte biomarker concentrations is essential. This study presents a non-invasive approach for quantifying various breast cancer biomarkers—including human epidermal growth factor receptor II (HER-II) (2+, 3+ (I), 3+ (II), 3+ (III), and positive IV) and CA 15-3—using a directional, plasmonically active, label-free SERS sensor. Each stage of sensor functionalization, conjugation, and biomarker interaction was verified by UV–Vis spectroscopy. Atomic force microscopy (AFM) characterized the morphology of gold nanourchin (GNU)-immobilized printed circuit board (PCB) substrates. An enhancement factor of ≈ 0.5 × 10⁵ was achieved using Rhodamine 6G as the probe molecule. Calibration curves were initially established using standard HER-II solutions at concentrations ranging from 1 to 100 ng/mL and CA 15-3 at concentrations from 10 to 100 U/mL. The SERS signal intensities in the 620–720 nm region were plotted against concentration, yielding linear sensitivity with R² values of 0.942 and 0.800 for HER-II and CA15-3, respectively. The same procedure was applied to breast cancer serum (BCS) samples, allowing unknown biomarker concentrations to be determined based on the corresponding calibration curves. SERS data were processed using the filtfilt filter from scipy.signal for smoothing and then baseline-corrected with the Improved Asymmetric Least Squares (IASLS) algorithm from the pybaselines.Whittaker library. Principal Component Analysis (PCA) effectively distinguished the sample groups and revealed spectral differences before and after biomarker interactions. Key Raman peaks were attributed to functional groups including N–H (primary and secondary amines), C–H antisymmetric stretching, C–N (amines), C=O antisymmetric stretching, NH₃⁺ (amines), carbohydrates, glycine, alanine, amides III, C=N stretches, and NH₂ in primary amides. Full article

Noise-induced hearing loss (NIHL) is a significant occupational health issue, characterized by permanent damage to the cochlea due to prolonged exposure to high-intensity noise. Circulating microRNAs (c-miRNAs) have emerged as promising non-invasive indicators of inner ear pathology and potential modulators of cellular stress responses. Nevertheless, their specific roles in NIHL remain inadequately characterized. This study evaluated miRNA expression in the peripheral blood of individuals with bilateral NIHL (n = 12) and matched healthy controls (n = 6) using GeneChip^® miRNA 4.0 arrays. The Transcriptome Analysis Console software was used for differential expression analysis, and bioinformatic predictions of gene targets and pathway enrichment were performed using TargetScan (version 8.0) and the Enrichr tool. Among the 72 differentially expressed miRNAs (FDR < 0.05), hsa-miR-486-2, hsa-miR-664b-3p, hsa-miR-4485, hsa-miR-501, and hsa-miR-663b were notably upregulated, while hsa-miR-6723, hsa-miR-194-2, hsa-miR-668-5p, hsa-miR-4722-3p, and hsa-miR-4716 showed significant downregulation. Enrichment analyses indicated involvement in apoptosis regulation, mitochondrial stability, and cell cycle control. Principal component analysis (PCA) and clustering methods revealed clear molecular distinctions between the patient and control groups. The observed alterations in c-miRNA profiles highlight their relevance to NIHL-related cellular stress and degeneration. These findings support their utility as candidate biomarkers for diagnosis and prognosis, warranting further validation in functional and longitudinal studies. Full article

Background/Objectives: This study evaluates whether combining ⁶⁸Ga-PSMA-11-PET/CT derived imaging biomarkers with clinical risk factors improves the prediction of early biochemical recurrence (eBCR) or clinical progress in patients with high-risk prostate cancer (PCa) after primary treatment, using machine learning (ML) models. Methods: We analyzed data from 93 high-risk PCa patients who underwent ⁶⁸Ga-PSMA-11 PET/CT and received primary treatment at a single center. Two predictive models were developed: a logistic regression (LR) model and an ML derived probabilistic graphical model (PGM) based on a naïve Bayes framework. Both models were compared against each other and against the CAPRA risk score. The models’ input variables were selected based on statistical analysis and domain expertise including a literature review and expert input. A decision tree was derived from the PGM to translate its probabilistic reasoning into a transparent classifier. Results: The five key input variables were as follows: binarized CAPRA score, maximal intraprostatic PSMA uptake intensity (SUVmax), presence of bone metastases, nodal involvement at common iliac bifurcation, and seminal vesicle infiltration. The PGM achieved superior predictive performance with a balanced accuracy of 0.73, sensitivity of 0.60, and specificity of 0.86, substantially outperforming both the LR (balanced accuracy: 0.50, sensitivity: 0.00, specificity: 1.00) and CAPRA (balanced accuracy: 0.59, sensitivity: 0.20, specificity: 0.99). The decision tree provided an explainable classifier with CAPRA as a primary branch node, followed by SUVmax and specific PET-detected tumor sites. Conclusions: Integrating ⁶⁸Ga-PSMA-11 imaging biomarkers with clinical parameters, such as CAPRA, significantly improves models to predict progression in patients with high-risk PCa undergoing primary treatment. The PGM offers superior balanced accuracy and enables risk stratification that may guide personalized treatment decisions. Full article

Rising water temperatures due to climate change pose a significant threat to Phoxinus lagowskii, a cold-water fish that is ecologically vital to the high-latitude regions of China. This study assessed heat stress effects on behavioral, hematological, histological, physiological, and molecular responses in P. lagowskii. The critical maximum temperature (CT_max) was determined using the loss of equilibrium (LOE) method, with the CT_max reaching 29 °C. Elevated temperatures lead to an increase in the OBR. Fish were subjected to acute heat stress at 28 °C (below CT_max) for 48 h, with samples collected during the 48 h period. RBC, WBC, HGB, and HCT significantly increased during heat stress but decreased 12 h after heat stress. The levels of serum cortisol and blood glucose after heat stress were significantly higher than those in the control group. After heat stress, the height of the ILCM in the gills increased significantly, and the liver exhibited vacuolar degeneration and hypopigmentation. The activities of Na⁺-K⁺-ATPase and Ca²⁺-Mg²⁺-ATPase in the gills initially increased and then decreased over the duration of heat stress. Most enzyme activities (PK, LDH, PFK, and HK) decreased during heat stress, while LPL and HL levels increased, indicating that lipid metabolism was the primary utilization process under heat stress. There was an increase in SOD activity at 12 h, followed by a decrease at 24 h, and an increase in CAT activity under heat stress. Integrated biomarker response (IBR) and principal component analysis (PCA) were employed to synthesize multi-level responses. The IBR values reached their peak at 3 h and 48 h of heat stress. We observed an upregulation of heat shock proteins (Hsp70, Hsp90, and Hsc70) as well as interleukin-10 (IL-10) in response to heat stress. Our findings offer novel insights into the mechanisms underlying the heat stress response in P. lagowskii, thereby enhancing our understanding of the effects of heat stress on cold-water fish. Full article

Cancer is hallmarked by uncontrolled cell proliferation and enhanced cell survival, driven by a complex interplay of factors—including genetic and epigenetic changes—that disrupt metabolic and signaling pathways and impair organelle function. While the roles of mitochondria and the endoplasmic reticulum in cancer are widely recognized, emerging research is now drawing attention to the involvement of peroxisomes in tumor biology. Peroxisomes are essential for lipid metabolism, including fatty acid α- and β-oxidation, the synthesis of docosahexaenoic acid, bile acids, and ether lipids, as well as maintaining redox balance. Despite their critical functions, the role of peroxisomes in oncogenesis remains inadequately explored. Prostate cancer (PCa), the second most common cancer in men worldwide, exhibits a unique metabolic profile compared to other solid tumors. In contrast to the glycolysis-driven Warburg effect, primary PCa relies primarily on lipogenesis and oxidative phosphorylation. Peroxisomes are intricately involved in the metabolic adaptations of PCa, influencing both disease progression and therapy resistance. Key alterations in peroxisomal activity in PCa include the increased oxidation of branched-chain fatty acids, upregulation of α-methylacyl coenzyme A racemase (a prominent PCa biomarker), and downregulation of 1-alkyl-glycerone-3-phosphate synthase and catalase. This review critically examines the role of peroxisomes in PCa metabolism, progression, and therapeutic response, exploring their potential as biomarkers and targets for therapy. We also consider their relationship with androgen receptor signaling. A deeper understanding of peroxisome biology in PCa could pave the way for new therapies to improve patient outcomes. Full article

Background/Objectives: Radiolabeled biomolecules specifically targeting overexpressed structures on tumor cells hold great potential for prostate cancer (PCa) imaging and therapy. Due to heterogeneous target expression, single radiopharmaceuticals may not detect or treat all lesions, while simultaneously applying two or more radiotracers potentially improves staging, stratification, and therapy of cancer patients. This study explores a dual-tracer SPECT approach using [¹¹¹In]In-RM2 (targeting the gastrin-releasing peptide receptor, GRPR) and [^99mTc]Tc-PSMA-I&S (targeting the prostate-specific membrane antigen, PSMA) as a proof of concept. To mimic heterogeneous tumor lesions in the same individual, we aimed to establish a dual xenograft mouse model for preclinical evaluation. Methods: CHO-K1 cells underwent lentiviral transduction for human GRPR or human PSMA overexpression. Six-to-eight-week-old female immunodeficient mice (NOD SCID) were subsequently inoculated with transduced CHO-K1 cells in both flanks, enabling a dual xenograft with similar target density and growth of both xenografts. Respective dual-isotope imaging and γ-counting protocols were established. Target expression was analyzed ex vivo by Western blotting. Results: In vitro studies showed similar target-specific binding and internalization of [¹¹¹In]In-RM2 and [^99mTc]Tc-PSMA-I&S in transduced CHO-K1 cells compared to reference lines PC-3 and LNCaP. However, in vivo imaging showed negligible tumor uptake in xenografts of the transduced cell lines. Ex vivo analysis indicated a loss of the respective biomarkers in the xenografts. Conclusions: Although the technical feasibility of a dual-tracer SPECT imaging approach using ¹¹¹In and ^99mTc has been demonstrated, the potential of [^99mTc]Tc-PSMA-I&S and [¹¹¹In]In-RM2 in a dual-tracer cocktail to improve PCa diagnosis could not be verified. The animal model, and in particular the transduced cell lines developed exclusively for this project, proved to be unsuitable for this purpose. The in/ex vivo experiments indicated that results from an in vitro model may not necessarily be successfully transferred to an in vivo setting. To assess the potential of this dual-tracer concept to improve PCa diagnosis, optimized in vivo models are needed. Nevertheless, our strategies address key challenges in dual-tracer applications, aiming to optimize future SPECT imaging approaches. Full article