Search Results (321)

Background/Objectives: Ergot alkaloids are mycotoxins produced mainly by fungi of the genus Claviceps, infecting a wide variety of plants, especially cereals. These toxins usually manifest as black, hardened sclerotia (ergots), though they may also be invisible when dispersed in grain. They pose a significant risk to animals and humans when present in contaminated cereals. They can cause ergotism, with vasoconstriction, ischemia, hallucinations, and in severe cases gangrene. This study was carried out in response to the European legislative actions which determine the permissible levels of ergot alkaloids in cereals. Historically, consumers manually removed visible sclerotia from grain, and farmers applied fertilizers or timed harvests to specific periods to mitigate contamination. However, these traditional methods have proven insufficient. We therefore explored advanced techniques for detecting and quantifying ergot-contaminated cereals, as well as methods for reducing ergot alkaloid concentrations. Methods: Searches were conducted in scientific databases including Google Scholar, PubMed, and Scopus to identify research articles, reviews, and experimental studies published mainly between 2012 and August 2025, including accepted or in-press manuscripts, with special attention to works from 2021 onward to capture the most recent advancements. Results/Conclusions: Ultra-high-performance liquid chromatography–tandem mass spectrometry (UHPLC-MS/MS) is the reference method for confirmatory, epimer-aware quantification of ergot alkaloids, and is already standardized. Recent QuEChERS-UHPLC-MS/MS workflows in cereal matrices, including oat-based products, routinely achieve limits of quantification of about 0.5–1.0 µg/kg with single-run analysis times of about 5–15 min. Rapid screening options complement, rather than replace, confirmatory mass spectrometry: magnetic bead-based immunoassays that use magnetic separation and a smartphone-linked potentiostat provide sub-hour turnaround and field portability for trained quality-assurance staff, although external validation and calibration traceable to LC-MS/MS remain prerequisites for routine use. In practice, operators are adopting tiered, orthogonal workflows (e.g., immunoassay or electronic-nose triage at intake followed by DNA-based checks on grain washings and LC–MS/MS confirmation, or hydrazinolysis “sum parameter” screening followed by targeted MS speciation). Such combinations reduce turnaround time while preserving analytical rigor. Biotechnology also offers potential solutions for reducing ergot alkaloid concentrations at the source. Finally, to enhance consumer safety, artificial intelligence and blockchain-based food traceability appear highly effective. These systems can connect all stakeholders from producers to consumers, allowing for real-time updates on food safety and rapid responses to contamination issues. This review primarily synthesizes advances in analytical detection of ergot alkaloids, while mitigation strategies and supply chain traceability are covered concisely as supporting context for decision making. Full article

High-grade serous ovarian cancer (HGSOC) is the most common (~80%) and lethal ovarian cancer subtype in the United States, characterized by TP53 mutations and DNA repair defects causing chromosomal instability (CIN). KIF18A is an essential cytoskeletal motor protein for cell division in CIN+ cancer cells, but it is not necessary for cell division in normal cells. Therefore, KIF18A represents a promising target for therapeutic interventions in CIN+ cancers. We investigated the use of a novel KIF18A inhibitor ATX020, for selectively targeting CIN+ HGSOC cells using growth inhibition assays, invasion assays, immunoassays, cell cycle analysis, and immunofluorescence techniques. Using DepMap and flow cytometry, we classified a panel of HGSOC cell lines based on aneuploidy scores (AS) and ploidy levels and identified a correlation between these classifications and sensitivity against ATX020. ATX020 induced cytotoxicity through mitotic arrest and DNA damage, and reduced tumor growth in HGSOC with high aneuploidy scores (AS). Mechanistically, ATX020 blocks KIF18A’s plus-end movement on spindle fibers, increasing spindle length, resulting in chromosomal mis-segregation, aneuploidy, and DNA damage. Our findings suggest that ATX020 inhibits CIN+ HGSOC cells mainly by inducing mitotic arrest and DNA damage, disrupting KIF18A’s function crucial for mitosis. Full article

Background: Neurofilament light chain (NfL) is a well-established biomarker of neuroaxonal damage, detectable in serum through immunoassays. Its potential relevance in psychiatric conditions, including anorexia nervosa (AN), is currently under investigation. This study aims to quantify serum NfL levels in individuals with AN, evaluate their correlation with autoantibodies detection, and critically examine the specificity of NfL as a biomarker in this context. Methods: A total of 100 participants were enrolled, comprising 50 individuals diagnosed with AN and 50 age-matched, normal-weight controls. Serum concentrations of NfL and immunoglobulin G (IgG) antibodies reactive to hypothalamic antigens were measured using validated immunoassay techniques. Results: Serum NfL concentrations were markedly higher in the AN group compared to healthy controls. Interestingly, NfL levels tended to decrease with longer disease duration and with the recovery of body mass index (BMI), indicating a possible association between clinical improvement and reduced neuroaxonal damage. Furthermore, the results confirmed the presence of anti-hypothalamic autoantibodies and revealed a positive correlation between their levels and serum NfL concentrations. Conclusions: Clinical remission in AN appears to be linked to a decrease in both markers neuronal damage and hypothalamic autoimmunity. However, as elevated serum NfL is observed across a spectrum of neurological and psychiatric disorders, its specificity as a biomarker for AN should be further investigated. While NfL may reflect neuroaxonal injury in AN, its interpretation should be contextualized within a broader clinical and immunological framework. Full article

Acetochlor is a selective herbicide affecting weeds of cereal plants. Its analysis in soils allows accessing their suitability for crops and risks of contamination of agricultural products. The aim of this study was to develop a microplate enzyme immunoassay for the determination of acetochlor in soil extracts. For the development, rabbit antibodies specific to acetochlor were obtained by immunization with a conjugate of carrier protein with a derivative of acetochlor with mercaptopropionic acid. Another derivative with mercaptosuccinic acid was applied for immobilization on the solid phase. In the study, organic extracts have been obtained from soil varying solvents and their ratios, and using QuEChERS protocol. The extracts have been tested to estimate residual influences of the sample matrix. Optimal conditions for the immunoassay were selected, appropriate sample preparation techniques, and the composition of the medium for competitive immune interaction. The most effective approach involved dichloromethane extraction, followed by careful evaporation and subsequent reconstitution of the dry residue in a 10 mM phosphate-buffer solution supplemented with 0.1% gelatin. The resulting analytical system exhibited a detection limit of 59.4 ng/mL for acetochlor, with a working range spanning from 112 to 965 ng/mL. Taking into account the soil sample preparation, the LOD was estimated as 0.3 µg/g with the working range from 0.66 to 5.7 µg/g of soil. Analysis of prepared extracts from gray forest soil demonstrated a revealing of acetochlor between 74% and 124%. Full article

Multiple-mode immunoassays have the advantages of self-correction, self-validation, and high accuracy and reliability. In this work, we developed a strategy for the design of triple-mode immunoassays with the self-assemblies of three-in-one small molecules as signal reporters. Pyrroloquinoline quinone (PQQ), with a well-defined redox peak and excellent spectroscopic and fluorescent signals, was chosen as the signaling molecule. PQQ was coordinated with Cu²⁺ to form metal–organic nanoparticle as the signal label. Hexahistidine (His₆)-tagged recognition element (recombinant streptavidin) was attached to the Cu-PQQ surface through metal coordination interaction between the His₆ tag and the unsaturated metal site. The captured Cu-PQQ nanoparticle released a large number of PQQ molecules under an acidic condition, which could be simultaneously monitoring by electrochemical, UV-vis, and fluorescent techniques, thereby allowing for the development of triple-model immunoassays. The three methods were used to determine the concentration of carcinoembryonic antigen (CEA) with the detection limits of 0.01, 0.1, and 0.1 ng/mL, respectively. This strategy opens up a universal route for the preparation of multiple-model signal labels and the oriented immobilization of bioreceptors for molecular recognition. Full article

Background: Patients with chronic lymphocytic leukemia (CLL) who were not receiving treatment were included in this experimental prospective correlation study. We aimed to elucidate the complex relationship between smudge cells, surface CD20, and soluble CD20 in CLL patients. Methods: We created blood smears from blood samples collected from our patients using a manual technique consistently performed by the same technician. The May–Grunwald Giemsa dye was used to stain all of the slides. The B-cell phenotypic was analyzed using the FacsCanto II flow cytometer (Becton Dickinson, CA, USA) at the time of diagnosis. Competitive Enzyme-Linked Immunoassay (ELISA) was used to quantitatively assess the amounts of soluble CD20/MS4A1. Results: The percentage of smudge cells and soluble CD20 antigen levels were shown to be significantly inversely correlated, suggesting a considerable link (correlation coefficient (r) = −0.51, p = 0.006). Similarly, a significant inverse relationship (r = −0.36, p = 0.04) was found by the Spearman correlation test between the smudge cell ratio and CD20 median fluorescence intensity (MFI) on cell surfaces. Soluble CD20/MS4A1 and surface CD20 MFI were shown to have a weakly positive association that was almost statistically significant (Spearman’s rho = 0.34, p = 0.064). With a sensitivity of 69% and specificity of 86%, we discovered that a cut-off value of 2.2 ng/dL for soluble CD20 predicted higher smudge cells (area under the curve (95% confidence interval (CI)): 0.75 (0.57 to 0.93), p = 0.021). Conclusions: We found a significant inverse association between smudge cells and both surface CD20 and soluble CD20/MS4A1 in our study examining the correlation between smudge cells, soluble CD20, and CD20/MS4A1 in CLL patients. Our findings indicate that soluble CD20 may contribute to understanding the pathophysiology of smudge cells and could be further investigated as a potential prognostic marker in CLL. Full article

Simultaneous extraction of multiple clinically relevant, specific allergens from complex, processed foods for immunoassay quantification remains challenging. Moreover, shared extraction buffers remain elusive, which limits the effectiveness of multiplex (multi-target) technology. This study aimed to optimise extraction of specific allergens from challenging food samples and identify shared methods of extraction for multiplex analysis. Incurred processed food matrices (chocolate dessert, raw and baked biscuit) were prepared and extracted with 10 different buffers varying in base, pH, and additive content. Extracts were analysed by allergen-specific multiplex array and ELISA. Optimised recovery of 14 food allergens was obtained from complex incurred matrices using two extraction buffers: 50 mM carbonate bicarbonate with 10% fish gelatine, and PBS with 2%-Tween, 1 M NaCl with 10% fish gelatine and 1% PVP. In most cases, optimised buffers provided 50–150% recovery from incurred foods. Matrices that contained chocolate or were subject to thermal processing had lower recoveries. Optimised allergen extraction methods can be used to analyse foods associated with treatment and prevention of allergy and reference materials for clinically relevant allergen content and identify allergen contamination. Identification of shared, optimised extraction buffers will result in increased sample throughput in multiplex immunoassay techniques. Full article

Periprosthetic joint infection (PJI) remains a major complication in orthopedic surgery, with accurate and timely diagnosis being essential for optimal patient management. Traditional culture-based diagnostics are often limited by suboptimal sensitivity, especially in biofilm-associated and low-virulence infections. In recent years, non-culture-based methodologies have gained prominence. Molecular techniques, such as polymerase chain reaction (PCR) and next-generation sequencing (NGS), offer enhanced detection of microbial DNA, even in culture-negative cases, and enable precise pathogen identification. In parallel, extensive research has focused on biomarkers, including systemic (e.g., C-reactive protein, fibrinogen, D-dimer), synovial (e.g., alpha-defensin, calprotectin, interleukins), and pathogen-derived markers (e.g., D-lactate), the latter reflecting metabolic products secreted by microorganisms during infection. The development of multiplex platforms now allows for the simultaneous measurement of multiple synovial biomarkers, improving diagnostic accuracy and turnaround time. Furthermore, the integration of artificial intelligence (AI) and machine learning algorithms into diagnostic workflows has opened new avenues for combining clinical, molecular, and biochemical data. These models can generate probability scores for PJI diagnosis with high accuracy, supporting clinical decision-making. While these technologies are still being validated for routine use, their convergence marks a significant step toward precision diagnostics in PJI, potentially improving early detection, reducing diagnostic uncertainty, and guiding targeted therapy. Full article

Food contamination poses a significant global public health challenge, necessitating the accurate detection of hazardous substances within complex food matrices. Magnetic core–shell nanomaterials have emerged as critical materials for trace contaminant analysis due to their efficient magnetic separation capabilities, excellent adsorption performance, and tunable surface functionalities. By encapsulating magnetic cores with functional shells, these nanomaterials combine rapid magnetic responsiveness with advantageous shell properties, including target-specific recognition, enhanced dispersibility, colloidal stability, and high surface area. This enables a comprehensive detection approach encompassing target adsorption, rapid separation, and signal amplification. Magnetic core–shell nanomaterials have been effectively integrated with techniques including magnetic solid-phase extraction (MSPE), fluorescence (FL) assays, and lateral flow immunoassays (LFIAs), demonstrating broad applicability in food safety monitoring and detection. This review outlines synthesis strategies for magnetic core–shell nanomaterials, highlights their applications for food contaminant detection, and discusses future challenges and prospects in the field of food safety analysis. Full article

To evaluate the analytical performance and clinical utility of the automated fluorescence-based POC immunoassay system (AFIAS), compared with established enzyme-linked immunosorbent assay (ELISA) methods for measuring adalimumab and anti-adalimumab antibodies (AAAs) in patients with rheumatoid arthritis and ankylosing spondylitis. 96 patients receiving adalimumab for rheumatoid arthritis (RA) or ankylosing spondylitis (AS) were consecutively recruited. Measurements of adalimumab trough levels and AAAs were taken before the patients’ scheduled adalimumab injection. Three ELISA techniques (RIDASCREEN^®, IDKmonitor^®, and LISA TRACKER) were compared with the AFIAS method. Statistical analyses included Bland–Altman, Passing–Bablok regression, kappa values, and intraclass correlation coefficients. Clinical and demographic characteristics were examined to determine the association between adalimumab concentration and AAA detection. The diagnoses included 58 RA diagnoses and 38 AS diagnoses. The median concentrations were 9.33, 7.4, 7.4, and 9.38 µg/mL for RIDASCREEN, IDKmonitor, LISA TRACKER, and AFIAS, respectively. Strong correlations were observed between the techniques. Bland–Altman analysis revealed bias differences of 0.85, 2.03, and 2.76 µg/mL, and the Passing–Bablok regression slopes were 1.046, 1.391, and 1.274 for RIDASCREEN, IDKmonitor, and LISA TRACKER, respectively, compared with AFIAS. Agreement in AAA detection showed kappa values of 0.81 and 0.75 for AFIAS versus IDKmonitor and LISA TRACKER, respectively. A high body mass index, extended injection interval, and RA diagnosis were associated with low adalimumab concentrations in the multivariate analysis. Antinuclear antibody positivity, a higher rheumatoid factor, and disease activity were associated with AAA positivity in univariate analysis. The AFIAS POC measurement method demonstrated time-efficient and highly agreeable results for adalimumab and AAA measurements compared with the results of commercial ELISA methods. Full article

Fentanyl and its analogues represent a severe threat due to their extreme potency and increasing prevalence in illicit drug supplies. Even trace amounts (on the order of a couple of milligrams) can be lethal, contributing to a surge in opioid overdose deaths worldwide. Beyond the public health crisis, fentanyl has emerged as a security concern, with the potential for deliberate use as a chemical agent in CBRN scenarios. This underscores the critical need for rapid and accurate detection methods that can be deployed by security forces and first responders. Modern technology offers a range of solutions—from portable mass spectrometers and spectroscopic devices to electrochemical sensors and immunoassay kits—that enable on-site identification of fentanyl and its analogues. This review provides a comprehensive overview of detection techniques, examining their capabilities and applications in law enforcement, border control, and CBRN incident response. We highlight how integration of advanced sensors with machine learning is enhancing detection accuracy in complex field environments. Challenges such as operational constraints and the ever-evolving variety of fentanyl analogues are discussed, and future directions are recommended to improve field-deployable detection tools for safety and security applications. Full article

The increasing demand for high-throughput, real-time, and single-molecule protein analysis in precision medicine has propelled the development of novel sensing technologies. Among these, nanopore-based methods have garnered significant attention for their unique capabilities, including label-free detection, ultra-sensitivity, and the potential for miniaturization and portability. Originally designed for nucleic acid sequencing, nanopore technology is now being adapted for peptide and protein analysis, offering promising applications in biomarker discovery and disease diagnostics. This review examines the latest advances in biological, solid-state, and hybrid nanopores for protein sensing, focusing on their ability to detect amino acid sequences, structural variants, post-translational modifications, and dynamic protein–protein or protein–drug interactions. We critically compare these systems to conventional proteomic techniques, such as mass spectrometry and immunoassays, discussing advantages and persistent technical challenges, including translocation control and signal deconvolution. Particular emphasis is placed on recent advances in protein sequencing using biological and solid-state nanopores and the integration of machine learning and signal-processing algorithms that enhance the resolution and accuracy of protein identification. Nanopore protein sensing represents a disruptive innovation in biosensing, with the potential to revolutionize clinical diagnostics, therapeutic monitoring, and personalized healthcare. Full article

The lateral flow immunoassay (LFIA) is a widely utilized, rapid diagnostic technique characterized by its short analysis duration, cost efficiency, visual result interpretation, portability and suitability for point-of-care applications. However, conventional LFIAs have limited sensitivity, a challenge that can be overcome by the introduction of gold nanoparticles, which provide enhanced sensitivity and selectivity (compared, for example, to latex beads or carbon nanoparticles) for the detection of target analytes, due to their optical properties, chemical stability and ease of functionalization. In this work, gold nanoparticle-based LFIAs are developed for the detection of aflatoxin B1, and the relative performance of different morphology particles is evaluated. LFIA using gold nano-labels allowed for aflatoxin B1 detection over a range of 0.01 ng/mL–100 ng/mL. Compared to spherical gold nanoparticles and gold nano-flowers, star-shaped gold nanoparticles show increased antibody binding efficiency of 86% due to their greater surface area. Gold nano-stars demonstrated the highest sensitivity, achieving a limit of detection of 0.01ng/mL, surpassing the performance of both spherical gold nanoparticles and gold nano-flowers. The use of star-shaped particles as nano-labels has demonstrated a five-fold improvement in sensitivity, underscoring the potential of integrating diverse nanostructures into LFIA for significantly improving analyte detection. Moreover, the robustness and feasibility of gold nano-stars employed as labels in LFIA was assessed in detecting aflatoxin B1 in a wheat matrix. Improved sensitivity with gold nano-stars holds promise for applications in food safety monitoring, public health diagnostics and rapid point-of-care diagnostics. This work opens the pathway for further development of LFIA utilizing novel nanostructures to achieve unparallel precision in diagnostics and sensing. Full article

Salivary α-amylase, primarily encoded by the AMY1 gene, initiates the enzymatic digestion of dietary starch in the oral cavity and has recently emerged as a potential biomarker in metabolic research. Variability in salivary amylase activity (SAA), driven largely by copy number variation of AMY1, has been associated with postprandial glycemic responses, insulin secretion dynamics, and susceptibility to obesity. This review critically examines current analytical approaches for quantifying SAA, including enzymatic assays, colorimetric techniques, immunoassays, and emerging biosensor technologies. The methodological limitations related to sample handling, intra-individual variability, assay standardization, and specificity are highlighted in the context of metabolic and clinical studies. Furthermore, the review explores the physiological relevance of SAA in energy homeostasis and its associations with visceral adiposity and insulin resistance. We discuss the potential integration of SAA measurements into obesity risk stratification and personalized dietary interventions, particularly in individuals with altered starch metabolism. Finally, the review identifies key research gaps and future directions necessary to validate SAA as a reliable metabolic biomarker in clinical practice. Understanding the diagnostic and prognostic value of salivary amylase may offer new insights into the prevention and management of obesity and related metabolic disorders. Full article

Circadian rhythms are intrinsic, with roughly 24 h oscillations that coordinate many physiological functions and are increasingly recognized as key determinants of human health. When these rhythms become misaligned, there is an increased risk for neurodegenerative and psychiatric disorders, metabolic syndrome, sleep disturbances, and even certain cancers. The hormones, melatonin that rises in the evening and cortisol that peaks shortly after awakening, represent crucial biochemical markers of the circadian phase. This review systematically evaluates contemporary techniques for quantifying melatonin and cortisol, comparing biological matrices (blood, saliva, urine) alongside analytical platforms. Special focus is placed on two clinically informative markers: Dim Light Melatonin Onset (DLMO) and the Cortisol Awakening Response (CAR). We compared immunoassays with liquid chromatography tandem mass spectrometry (LC MS/MS), highlighting differences in sensitivity, specificity, and laboratory feasibility. Potential confounders, including ambient light, body posture, and exact sampling times—are discussed in detail, to show the capacity of providing the most reliable results. By emphasizing the need for standardized protocols and controlled sampling conditions, this review provides essential guidance for researchers and clinicians aiming to assess the circadian biomarkers melatonin and cortisol with precision since they can be used in clinical practice as diagnostic and prognostic tools for assessing numerous pathologies. Full article