Search Results (94)

Artificial insemination (AI) in rabbits depends largely on chilled semen storage, but the physiological responses to chilling and associated biochemical changes in seminal plasma (SP) remain poorly understood, particularly across breeds. This study aimed to compare the semen preservation capacity of Algerian local population (LAP) and New Zealand White (NZW) rabbits and to explore the relationship between SP oxidative stress biomarkers and sperm traits during 72 h of chilled storage at 5 °C. Semen pools (nine/breed) were evaluated at 0, 4, 24, 48, and 72 h for motility, viability, and acrosome status. Oxidative stress markers were also assessed in the SP, including malondialdehyde (MDA), reactive oxygen metabolites (ROMs), superoxide dismutase (SOD), glutathione peroxidase (GPX), and catalase (CAT). LAP sperm showed higher motility (p < 0.001) and viability (p < 0.05), particularly between 4 h and 48 h, and exhibited a lower rate of acrosome reaction (p < 0.001) from 48 h to 72 h. Lower SOD and higher CAT activity in LAP (p < 0.001), correlated with MDA and acrosome status, respectively, may reflect a more balanced antioxidant response. Lipid peroxidation did not appear to be the main factor driving sperm deterioration (p > 0.05). These results demonstrate that LAP rabbits exhibit better resilience to chilled storage compared to NZW and highlight the potential value of CAT and SOD activities as indicators of sperm resilience during chilled storage. Further studies are required to validate and extend these findings, with the aim of improving semen preservation strategies. Full article

Male infertility is a multifactorial condition often associated with disruptions in sperm metabolism and mitochondrial function, yet traditional semen analysis provides limited insight into these molecular mechanisms. Understanding sperm bioenergetics and metabolic dysfunctions is crucial for improving the diagnosis and treatment of conditions such as asthenozoospermia and azoospermia. This systematic review synthesizes recent literature, focusing on advanced tools and techniques—including omics technologies, advanced imaging, spectroscopy, and functional assays—that enable comprehensive molecular assessment of sperm metabolism and development. The reviewed studies highlight the effectiveness of metabolomics, proteomics, and transcriptomics in identifying metabolic biomarkers linked to male infertility. Non-invasive imaging modalities such as Raman and magnetic resonance spectroscopy offer real-time metabolic profiling, while the seminal microbiome is increasingly recognized for its role in modulating sperm metabolic health. Despite these advances, challenges remain in clinical validation and implementation of these techniques in routine infertility diagnostics. Integrating molecular metabolic assessments with conventional semen analysis promises enhanced diagnostic precision and personalized therapeutic approaches, ultimately improving reproductive outcomes. Continued research is needed to standardize biomarkers and validate clinical utility. Furthermore, these metabolic tools hold significant potential to elucidate the underlying causes of previously misunderstood and unexplained infertility cases, offering new avenues for diagnosis and treatment. Full article

High semen quality is vital for reproductive success in the swine industry; however, seasonal fluctuations often compromise this quality. The molecular mechanism underlying these seasonal effects on semen quality remains largely unclear. This study employed untargeted metabolomic profiling of boar seminal plasma (SP) to identify metabolites and metabolic pathways associated with semen quality during the summer and winter months. Semen samples were collected from mature Duroc boars at a commercial boar stud and classified as Passed or Failed based on motility and morphology. SP from five samples per group was analyzed using ultra-high-performance liquid chromatography–mass spectrometry (UHPLC-MS). In total, 373 metabolites were detected in positive ion mode and 478 in negative ion mode. Several differentially expressed metabolites (DEMs) were identified, including ergothioneine, indole-3-methyl acetate, and avocadyne in the summer, as well as LysoPC, dopamine, and betaine in the winter. These metabolites are associated with key sperm functions, including energy metabolism, antioxidant defense, and capacitation. KEGG pathway analysis indicated enrichment in starch and sucrose metabolism, pyrimidine metabolism, and amino acid metabolism across the seasons. Overall, the results reveal that SP metabolomic profiles vary with the season, thereby influencing semen quality. The identified metabolites may serve as potential biomarkers for assessing semen quality and enhancing reproductive efficiency in swine production. Full article

The rapid evolution of deep learning (DL) has fundamentally transformed the paradigm for detecting special components in agricultural products, addressing critical challenges in food safety, quality control, and precision agriculture. This comprehensive review systematically analyzes many seminal studies to evaluate cutting-edge DL applications across three core domains: contaminant surveillance (heavy metals, pesticides, and mycotoxins), nutritional component quantification (soluble solids, polyphenols, and pigments), and structural/biomarker assessment (disease symptoms, gel properties, and physiological traits). Emerging hybrid architectures—including attention-enhanced convolutional neural networks (CNNs) for lesion localization, wavelet-coupled autoencoders for spectral denoising, and multi-task learning frameworks for joint parameter prediction—demonstrate unprecedented accuracy in decoding complex agricultural matrices. Particularly noteworthy are sensor fusion strategies integrating hyperspectral imaging (HSI), Raman spectroscopy, and microwave detection with deep feature extraction, achieving industrial-grade performance ($RPD$ > 3.0) while reducing detection time by 30–100× versus conventional methods. Nevertheless, persistent barriers in the “black-box” nature of complex models, severe lack of standardized data and protocols, computational inefficiency, and poor field robustness hinder the reliable deployment and adoption of DL for detecting special components in agricultural products. This review provides an essential foundation and roadmap for future research to bridge the gap between laboratory DL models and their effective, trusted application in real-world agricultural settings. 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

Sperm freezability exhibits marked individual variability, yet the mechanisms remain unclear. Using bulls as the experimental model, we integrated proteomic (sperm) and metabolomic (seminal plasma) analyses of high-freezability (HF) and control (CF) bulls to identify key biomarkers associated with sperm freezability. Post-thaw motility and membrane integrity were significantly higher in HF bulls (p < 0.05). Sperm proteome analysis revealed upregulated antioxidant proteins (PRDX2, GSTM4), heat shock proteins (HSP70, HSP90), and key enzymes in arginine and proline metabolism (PRODH, LAP3). Seminal plasma metabolomics revealed elevated spermine in HF bulls. Meanwhile, we found that spermine abundance was positively correlated with post-thaw motility, as well as with the expression levels of both PRODH and LAP3 (r > 0.6, p < 0.05). Functional validation demonstrated that 200 μM spermine supplementation in cryopreservation extenders enhanced post-thaw motility, kinematic parameters (VAP, VSL, VCL), membrane integrity, and acrosome integrity (p < 0.05). Concurrently, spermine enhanced antioxidant enzyme (SOD, CAT, GSH-Px) activity and reduced ROS and MDA levels (p < 0.05). Our study reveals a spermine-driven antioxidant network coordinating sperm–seminal plasma synergy during cryopreservation, offering novel strategies for semen freezing optimization. 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

Seminal plasma, traditionally regarded as a passive transport medium for sperm, has emerged as a sophisticated biofluid orchestrating critical dialogues in reproductive physiology. Contemporary research reveals its multifunctional role in modulating endometrial receptivity through molecular priming of the female reproductive tract, a process essential for successful embryo implantation. Notably, seminal plasma contains numerous extracellular vesicles (EVs) that serve as critical mediators of intercellular communication via the regulation of biological processes in target cells. Through this sophisticated vesicular communication system, seminal plasma extracellular vesicles (SPEVs) coordinate critical reproductive events. Thus, it will be important to elucidate the molecular mechanisms by which SPEVs mediate reproductive processes, to provide knowledge that may improve fertility outcomes. Herein, we elucidated the emerging potential of SPEVs as non-invasive biomarkers for male fertility assessment and infertility diagnosis. Furthermore, this review systematically summarized current advances in SPEVs, highlighting their multifaceted roles in mediating sperm maturation, regulating sperm capacitation, and modulating embryo implantation through targeted delivery of bioactive signaling molecules. Full article

Extracellular vesicles (EVs) contain bioactive substances and mediate a multitude of physiological functions. EVs can be found in most body fluids and are particularly abundant in semen. EVs have the potential to become a biomarker for the quality of boar semen. In this study, EVs were isolated from the semen of relatively young (10 months of age, Y-EVs) and old (30 months of age, O-EVs) duroc boars using ultracentrifugation. The isolated EVs were characterized using a transmission electron microscope, nanoparticle tracking analysis, and Western blotting. MicroRNA (miRNA) profiles and metabolomes were analyzed using high-throughput sequencing and liquid chromatography–mass spectrometry, respectively. The median particle sizes of Y-EVs and O-EVs were 151.3 nm and 162.1 nm, respectively. miR-148a-3p, miR-10b, miR-21-5p, miR-10a-5p, let-7a, etc., were identified as highly enriched miRNAs in seminal EVs of boars. Comparative analysis revealed 41 differentially expressed miRNAs and 132 differential metabolites between Y-EVs and O-EVs. Notably, 18 miRNAs were upregulated in O-EVs, such as miR-339-5p, miR-125a, miR-423-3p, and miR-29c, which were mainly enriched in endocytosis, focal adhesion, and adherens junction. KEGG pathway analysis further indicated that differential metabolites were enriched in glycerophospholipid metabolism. These results provide an insight into the functional roles of seminal EVs. Full article

Stroke affects approximately 12 million individuals annually, necessitating swift diagnosis to avert fatal outcomes. Current hospital imaging protocols often delay treatment, underscoring the need for portable diagnostic solutions. We have investigated silver nanostars (AgNS) incubated with human plasma, deposited on a simple aluminum foil substrate, and utilizing Surface-Enhanced Raman Spectroscopy (SERS) combined with machine learning (ML) to provide a proof-of-concept for rapid differentiation of stroke types. These are the seminal steps for the development of low-cost pre-hospital diagnostics at point-of-care, with potential for improving patient outcomes. The proposed SERS assay aims to classify plasma from stroke patients, differentiating hemorrhagic from ischemic stroke. Silver nanostars were incubated with plasma and spiked with glial fibrillary acidic protein (GFAP), a biomarker elevated in hemorrhagic stroke. SERS spectra were analyzed using ML to distinguish between hemorrhagic and ischemic stroke, mimicked by different concentrations of GFAP. Key innovations include optimized AgNS–plasma incubates formation, controlled plasma-to-AgNS ratios, and a low-cost aluminum foil substrate, enabling results within 15 min. Differential analysis revealed stroke-specific protein profiles, while ML improved classification accuracy through ensemble modeling and feature engineering. The integrated ML model achieved rapid and precise stroke predictions within seconds, demonstrating the assay’s potential for immediate clinical decision-making. Full article

Spermatogenesis is a complex process requiring precisely controlled metabolic adaptations. Although the genetic and cellular aspects of spermatogenesis have been extensively studied, the underlying metabolic mechanisms remain largely underexplored. In this study, we utilized STA-PUT technology to separate three key cell types involved in mouse spermatogenesis: pachytene spermatocytes (PAC), round spermatids (RS), and elongated spermatids (ES). A comprehensive untargeted metabolomic analysis revealed significant metabolic changes during spermatogenesis, such as reduced methylation-related metabolites and increased glycolytic intermediates and TCA cycle metabolites during ES. Moreover, metabolic differences between germ cells and somatic cells (Leydig and Sertoli cells) were highlighted, particularly in steroidogenesis and lipid metabolism. To investigate clinical relevance, we analyzed human seminal plasma. Samples from individuals with azoospermia displayed significant metabolic abnormalities, including reduced methionine, tryptophan, and arginine, which play vital roles in sperm development. Pathway enrichment analysis revealed disturbances in the metabolism of nucleotide, amino acid, and energy in azoospermia, suggesting potential biomarkers of male infertility. Our findings provide a comprehensive metabolic profile of spermatogenesis and suggest that metabolic alterations may be significant contributors to male infertility, particularly in cases of azoospermia. Full article

Sperm motility is a key factor influencing male fertility and is associated with metabolic and lipid profiles across species. The aim of this study was to investigate the relationship between sperm motility and the seminal plasma lipid profile in Simmental bulls, and to identify key lipids potentially influencing sperm motility. Semen samples were collected from 26 healthy Simmental bulls with an average age of 4.9 years. Sperm quality was evaluated using computer-assisted sperm analysis (CASA). Based on motility, the samples were divided into two groups: high sperm motility (HSM > 65%) and low sperm motility (LSM < 65%). Compared to the LSM group, the HSM group exhibited significantly higher sperm viability, motility, straight-line velocity, beat-cross frequency, and sperm acrosome integrity, while the sperm malformation rate was lower (p < 0.05). Lipid profiles were determined using LC-MS/MS, and 40 differential lipids were identified by multivariate statistical analysis. Among them, 39 lipids were upregulated in the LSM group compared to the HSM group. They were primarily triglycerides and carnitines, mainly involved in four metabolic pathways related to glycerophospholipid and linoleic acid metabolism. Notably, PC (16:0/20:4; 14:0/18:3), LPC (22:4/0:0; 22:6/0:0), and PE (14:0/18:1; 18:1/20:3) were diagnosed with great accuracy (AUC > 0.7), which means they may serve as potential biomarkers for sperm motility. Full article

Artificial insemination (AI), as an efficient assisted reproduction technology, can help the livestock industry to improve livestock and poultry breeds, optimize production performance and improve reproductive efficiency. AI technology has been widely used in pig production in China, but boar fertility affects the effectiveness of AI, and more and more studies have shown that there are significant differences in the fertility of boars with similar semen quality indicators. Therefore, this study aimed to identify biomarker molecules that indicate the level of boar fertility, which is important for improving the efficiency of AI. In this study, we collected 40 mL of ejaculates per boar used for extracellular vesicle (EV) characterization in 20 boars and identified 53 differentially expressed miRNAs by small RNA sequencing, of which 44 miRNAs were up-regulated in the high-fertility seminal EVs compared with low-fertility seminal EVs, and nine miRNAs were down-regulated. miR-26a was most significantly down-regulated in the high-fertility group compared to the low-fertility group, and it was hypothesized that this miRNA could be used as a biomolecular marker of semen reproductive performance. To further determine the effect of miR-26a on sperm function, we successfully established a miR-26a overexpression model and found that miR-26a reduced sperm viability, motility, acrosome integrity, plasma membrane integrity and ATP levels. Bioinformatics analysis and dual luciferase reporter analysis revealed that miR-26a directly targets High mobility group A1 (HMGA1). In conclusion, miR-26a can be used as a biomarker to identify high and low fertility in boar semen. Full article

Background: Seminal plasma is an important component of semen and has a significant effect on sperm function. However, the relationship between seminal plasma and sperm freezing capacity has not been fully studied. Purpose: Exploring metabolites and proteins related to the boar sperm freezing capacity in seminal plasma, by metabolomic and proteomic approaches, and directly verifying the protective effect of seminal plasma on the cryopreservation of boar sperm using high and low freezability seminal plasma as base freezing extender. Methods: Semen samples were collected from 30 different boars, 11 high and 11 low freezing-resistant boars were selected after freezing 2~4 times, and seminal plasma was selected at the same time. Sperm motility and movement parameters were analyzed using a CASA system. Reproductive hormones (Testosterone, progesterone, estradiol, prolactin, prostaglandin F2α, luteinoid hormone) in seminal plasma were detected by ELISA. Analysis of proteins and metabolites in high and low freezing-resistant seminal plasma by proteomics and metabolomics techniques. Results: The six reproductive hormones tested were not significantly associated with sperm freezing resistance. A total of 13 differentially expressed metabolites (DEMs) and 38 differentially expressed proteins (DEPs) were identified, while a total of 348 metabolites and 1000 proteins were identified. These DEMs were related to energy metabolism, drugs, or environmental pollutants, while the DEPs were mainly involved in the cytoskeletal dynamics and cell adhesion processes. There were 33 metabolites and 70 proteins significantly associated with mean progress motility (PM) at 10 min and 2 h after thawing. The 70 related proteins were associated with cell division and cycle regulation in gene ontology (GO) terms, as well as KEGG pathways, thermogeneration, and pyruvate metabolism. Using highly freezable boar SP as a base freezing extender made no difference from using lowly freezable boar SP, and both were not as good as the commercial control. Conclusion: There were significant differences in seminal plasma with different freezability, but the similarity was much greater than the difference. The protection effect of seminal plasma is not remarkable, and it does not exhibit superior cryoprotective properties compared to commercial semen cryoelongators. Significance: This study provides a deeper understanding of how seminal plasma composition affects sperm freezabilty. It provides potential biomarkers and targets for improving sperm cryopreservation techniques. Full article

Background/Objectives: Varicoceles are a common contributor to male infertility, significantly impacting male-factor infertility cases. Traditional diagnostic methods often lack the sensitivity to detect the molecular and cellular disruptions caused by varicoceles, limiting the development of effective, personalized treatments. This narrative review aims to explore the advancements in proteomics and metabolomics as innovative, non-invasive diagnostic tools for varicocele-associated male infertility and their potential in guiding personalized therapeutic strategies. Methods: A comprehensive literature search was conducted using databases such as PubMed, Scopus, and Web of Science up to October 2024. Studies focusing on the application of proteomic and metabolomic analyses in varicocele-associated male infertility were selected. The findings were critically analyzed to synthesize current knowledge and identify future research directions. Results: Proteomic analyses revealed differentially expressed proteins in the sperm and seminal plasma of varicocele patients, revealing disruptions in pathways related to oxidative stress, mitochondrial dysfunction, apoptosis, and energy metabolism. Key proteins such as heat shock proteins, mitochondrial enzymes, and apoptotic regulators were notably altered. Metabolomic profiling uncovered specific metabolites in seminal plasma—such as decreased levels of lysine, valine, and fructose—that correlate with impaired sperm function and fertility potential. The integration of proteomic and metabolomic data provides a comprehensive molecular fingerprint of varicocele-induced infertility, facilitating the identification of novel biomarkers for early diagnosis and the development of personalized therapeutic interventions. Conclusions: Advances in proteomics and metabolomics have significantly enhanced our understanding of the molecular mechanisms underlying varicocele-associated male infertility. These “omics” technologies hold great promise for improving diagnostic accuracy and personalizing treatment, ultimately leading to better outcomes for affected men. Future large-scale clinical trials and validations are essential to confirm these biomarkers and facilitate their integration into routine clinical practice. Full article