Antigen-antibody Reaction Research Articles

A bio-nano-engineered platform fabricated from cerium oxide-carbon nanoparticles stabilized with chitosan for label-free sensing of a lung cancer biomarker.

Herein, we report a label-free cancer biosensor designed for carcinoembryonic antigen (CEA) detection using a nanohybrid comprising CeO2 nanoparticles, carbon nanoparticles (CNPs), and chitosan (Ch). CeO2 nanoparticles were prepared using a simple green synthesis process. A thin film of the CeO2-CNPs-Ch nanohybrid was formed on indium tin oxide (ITO)-coated glass plates that endowed a high surface area, excellent stability, and good adsorption for the efficient loading of CEA antibodies. Quantitative and selective determination of CEA antigen was achieved by immobilizing monoclonal CEA antibodies (anti-CEA) on the CeO2-CNPs-Ch/ITO platform. The electrochemical response of the anti-CEA/CeO2-CNPs-Ch/ITO immunoelectrode was evaluated in a label-free immunoassay format using differential pulse voltammetry (DPV). The response studies of immunoelectrodes indicated wider linearity with respect to the CEA concentration in the range of 0.05-100 ng mL-1. The electrochemical cancer biosensor exhibited a higher sensitivity of 22.40 μA cm-2 per decade change in concentration along with storage stability for up to 35 days. The limit of detection (LOD) was 0.037 ng mL-1. Furthermore, this cancer biosensor exhibited good specificity and reproducibility. Thus, the proposed CeO2-CNPs-Ch nanocomposite-based platform provides an efficient method for the analysis of other antigen-antibody interactions and biomolecule detection. The efficacy of the anti-CEA/CeO2-CNPs-Ch/ITO immunoelectrode was further examined by measuring CEA levels in human serum.

Prediction of antibody-antigen interaction based on backbone aware with invariant point attention.

Antibodies play a crucial role in disease treatment, leveraging their ability to selectively interact with the specific antigen. However, screening antibody gene sequences for target antigens via biological experiments is extremely time-consuming and labor-intensive. Several computational methods have been developed to predict antibody-antigen interaction while suffering from the lack of characterizing the underlying structure of the antibody. Beneficial from the recent breakthroughs in deep learning for antibody structure prediction, we propose a novel neural network architecture to predict antibody-antigen interaction. We first introduce AbAgIPA: an antibody structure prediction network to obtain the antibody backbone structure, where the structural features of antibodies and antigens are encoded into representation vectors according to the amino acid physicochemical features and Invariant Point Attention (IPA) computation methods. Finally, the antibody-antigen interaction is predicted by global max pooling, feature concatenation, and a fully connected layer. We evaluated our method on antigen diversity and antigen-specific antibody-antigen interaction datasets. Additionally, our model exhibits a commendable level of interpretability, essential for understanding underlying interaction mechanisms. Quantitative experimental results demonstrate that the new neural network architecture significantly outperforms the best sequence-based methods as well as the methods based on residue contact maps and graph convolution networks (GCNs). The source code is freely available on GitHub at https://github.com/gmthu66/AbAgIPA .

A Comparative Study of the Rate of Helicobacter pylori infection Using Three Diagnostic Techniques among Patients in Rivers State

Background: Approximately 89% of gastric cancers are attributable to H. pylori infection, however, only 1 in 5 patients survive longer than 5 years after diagnosis. Evidence has shown that screening and eradication of H. pylori in young adults would be cost-effective and could prevent 1 gastric cancer in every 4 to 6 cases. Diagnostic tests used to detect Helicobacter pylori are either invasive or noninvasive methods. These tests have varying sensitivity and specificity, having about 90% accuracy in the diagnosis of H. pylori infection in clinical practice. Aim: This study aims at detecting Helicobacter pylori using antibody-antigen, stool antigen and polymerase chain reaction (PCR) and to compare the infection rate among the different diagnostic techniques. Methodology: This was a cross-sectional study. The participants included attendants to tertiary and private hospitals in Port Harcourt metropolis for medical treatment. Blood and stool samples were collected from one hundred and eighty (180) subjects aged 1 to 60 years and above. Ethical approval for the study was obtained from the Rivers State University Teaching Hospital Ethics Committee. Convenience sampling technique was deployed. Samples were collected from subjects who consented to the study. All samples collected were analyzed in Medical Microbiology Laboratory, Rivers State Teaching Hospital and subsequently, molecular analysis was carried out. Results: The blood antibodies test (serology) method shows a greater number of positive cases (43.9%) than stool antigen method (8.3%). Also, out of the 75% stool antigen positive samples, subjected to more specific PCR, only 25% were positive. There was a significant difference (p-value of <0.001) in sensitivity between antigen and serology, there was also a significant difference (p-value of <0.025) between antigen and PCR result outcomes. Conclusion: The study has shown that there is a notable variation in the diagnosis of H pylori among the laboratory techniques used.

Advanced label-free electrochemical immunosensor for a minimally invasive detection of proteins in paintings

In recent decades, scientific methodologies applied in theCultural Heritage field have been growing, due to their pivotal role in guiding informed decisions concerning conservation strategies and daily maintenance. To achieve this goal, minimally/non-invasive quantitative and qualitative analyses are needed. However, the non-invasive and selective identification of proteinaceous binders and coatings in artworks represent an open issue in Cultural Heritage science.Herein, a novel miniaturized system is introduced, which consists of a label-free electrochemical immunosensor integrated with biocompatible Gellan gel. This method is intended to selectively and minimally invasively identify ovalbumin (OVA) on-site in paintings. The label-free immunosensor is made up on screen-printed electrodes (SPEs) by functionalizing the working electrode (WE) with a primary antibody (anti-ovalbumin) for the specific recognition of OVA. The presence of OVA produces antigen-antibody reaction, which results in the development of a bulky immunocomplex on the WE. This complex is quantified using square wave voltammetry (SWV) and a reversible redox probe: the current measured is inversely proportional to the OVA concentrations. The developed immunosensors showed good analytical performances when applied directly to painted mock-ups, exhibiting a limit of detection (LOD) of 1.6 ng mL−1, a limit of quantification (LOQ) equal to 16 ng mL−1, a working range between 0.01 and 0.4 μg mL−1 and selectivity for OVA over other protein components commonly present in painted artworks, including bovine serum albumin (BSA), collagen, and casein.The outcomes highlighted the dependability of the immunosensor in detecting OVA and the efficacy of Gellan gel as a streamlined method for extracting the target protein while preventing residue accumulation on the painting surface. This advancement suggests the potential of Gellan gel-coupled immunosensor systems as viable diagnostic alternatives for artwork management and preservation.

Immunosensing of Cardiac Troponin I (cTnI) Using a Two-Electrode Electrochemiluminescence Platform with Near Persisting Luminescence Generated on a Ru(bpy)32+-Tripropylamine System.

An economical, rapid, and ultrasensitive detection of biomolecules in clinical settings is very crucial, particularly for the early detection of Cardiac Troponin I (cTnI), which is the gold standard biomarker for Acute Myocardial Infarction (AMI). Electrochemiluminescence (ECL) has risen in prominence as an important technique for in vitro diagnosis and detection by virtue of its high sensitivity reaching a femtomolar level. This study introduces an economically feasible nanoplatform for ECL immunosensing, consisting of a gold nanoparticle (AuNP) with Ru(bpy)32+ and tripropylamine (TPA) system, which is a potential ECL luminophore and coreactant system. AuNPs serve the role of an ECL signal enhancer as well as the carrier of antibody, which enables the creation of a label-free immunosensor for antigen-antibody interactions. The prepared immunosensor detected cTnI with a detection limit (LOD) of 0.03 ng/mL. This potential immunosensor provides appreciable results in the detection of cTnI from spiked real serum analysis, which shows its potential application in low-resource clinical settings.

Immuno-RCA for highly sensitive detection of the antigen-antibody complex in the blood group antigen model

The problem of detecting tiny quantities of analytes by immunochemical methods has been tried for a long time. One approach is to use nucleic acid amplification methods to amplify the signal from a single antigen-antibody interaction. An amplification method suitable for microarrays is the rolling circle amplification reaction. The principle of the method is usage of a conjugate of a detecting antibody with a primer and subsequent isothermal amplification. The generation of a huge single-stranded reaction product starts after adding the necessary components for amplification reaction: circular oligonucleotides, which serves as a template for amplification and phage phi29 polymerase with the other components. This reaction product consists of a lot of repeats of a nucleotide sequence, that is complementary to the circular template. The fluorescent DNA probe can hybridize to each repeat on the product molecule, resulting in a significantly higher level of fluorescence than with fluorescently labeled antibody or streptavidin development systems. In addition, the reaction product remains immobilized on the surface, allowing usage of this approach for the detection of antigen-antibody interactions in other solid-phase analysis systems, such as microarrays. A common problem with such approaches is the nonspecific sorption of components of the immunochemical reaction or amplification reaction, leading to a high background. It is obvious that no matter how highly sensitive the analysis is in theory, a high background will reduce the entire potential of the method to nothing. Herein, we have developed a method that makes it possible to detect small amounts of antibodies to glycans in blood serum and in swabs from tumor cells in a microarray format using a model of blood group antigens. It was possible to obtain a 30 to 70-fold increase of fluorescence level from a specific interaction compared to the use of fluorescently labeled streptavidin. The method we are developing is promising, as it allows us to significantly increase the signal from the specific antigen/antibody interaction in the glycochip format, which will make it possible to detect antibodies to glycans in samples with a very low concentrations of antibodies, for example, in washes from tumor cells.

Characteristics of the IgM Repertoires in the Peripheral Blood of Early Rheumatoid Arthritis Patients.

Rheumatoid arthritis is a common autoimmune disease, but little is known about the characteristics of the B cell repertoires in the peripheral blood. In this study, the peripheral IgM repertoires of early rheumatoid arthritis (ERA) patients were analyzed by high-throughput sequencing and bioinformatics analyses. Clonal expansion was observed in IgM repertoires of ERA patients. Interestingly, a subset of the dominant clones in ERA repertoires showed self- and poly-reactivity to several autoantigens. The clones were also present in IgM repertoires of healthy adults but they were not expanded, suggesting that may stem from the natural auto-reactive B cell repertoire. Additionally, the ERA repertoires exhibited a greater extent of somatic hypermutations, particularly in the ERA dominant clones, resulting in an enrichment of amino acids important for antigen-antibody interaction. The in-depth analysis of B-cell repertoires improved our knowledge of the IgM repertoires in early rheumatoid arthritis, offering potential insights into the disease's pathogenesis.

Molecular recognition characteristics of co-assembled peptides on atomically flat graphite surfaces

Molecular recognition, involving the binding of two or more molecules, is widely found in multiple disciplines. It plays a crucial role in driving specific molecular functionalization or biological activities such as antigen–antibody interactions. Recently, the molecular recognition of single peptides self-assembly at interfaces has been widely investigated since their broad applications in biosensors and bioelectronics. However, the recognition characteristics of peptide-peptide co-assembly on solids have not been investigated yet, which provides a basis for potential multi-probes biosensing or structure-intermingled functionalized bioelectronic applications. Here, we explored the molecular recognition characteristics of co-assembled peptides on two-dimensional (2D) layered nanomaterials, specifically graphite. Our findings showed distinct surface characteristics of peptide co-assembly in comparison to the independent peptide self-assembly. Peptide co-assembly exhibited the nucleation and growth heterogeneities with reduced nucleation and growth rates, dominated by a diffusion-limited step as confirmed via carrying out the sequential assembly experiments. Moreover, molecular dynamics simulation reveals a slowdown binding process of co-assembled peptides to graphite. Furthermore, the misattachment of one peptide to arrays of another type of peptide with distinct structural ordering orientations severely postponed peptide elongation. Therefore, our work provides valuable insight into the fundamental surface characteristics of two co-assembled peptides as they specifically recognize graphite surface via undergoing continuous surface behaviors from binding to diffusion until final ordering process. The formation of co-assembled peptide patterns on 2D layered nanomaterials incorporates multiple functions, enabling to provide potential applications in intermingled peptide-based biosensing or bioelectronic nanodevices.

Örjan Ouchterlony and the antigen-antibody double diffusion-in-gel: a survey.

The Swedish scientist Örjan Ouchterlony published four ground-breaking papers 1948-1966 in Acta Pathol Microbiol Scand where he described a new method of antigen-antibody reactions in gel. He described and defined the 'reaction of identity' and 'reaction of partial identity' when he used related antigens and 'reaction of non-identity' when he used non-related antigens. His results inspired scientists in other countries to further develop and modify the 'Ouchterlony method' which became useful for both scientific and clinical purposes. This survey describes how the methods were discovered and how they became modified and improved and how they were used, but also underlines that the original Ouchterlony method is still used.

Antibody sequence determinants of viral antigen specificity.

Throughout life, humans experience repeated exposure to viral antigens through infection and vaccination, resulting in the generation of diverse, antigen-specific antibody repertoires. A paramount feature of antibodies that enables their critical contributions in counteracting recurrent and novel pathogens, and consequently fostering their utility as valuable targets for therapeutic and vaccine development, is the exquisite specificity displayed against their target antigens. Yet, there is still limited understanding of the determinants of antibody-antigen specificity, particularly as a function of antibody sequence. In recent years, experimental characterization of antibody repertoires has led to novel insights into fundamental properties of antibody sequences but has been largely decoupled from at-scale antigen specificity analysis. Here, using the LIBRA-seq technology, we generated a large data set mapping antibody sequence to antigen specificity for thousands of B cells, by screening the repertoires of a set of healthy individuals against 20 viral antigens representing diverse pathogens of biomedical significance. Analysis uncovered virus-specific patterns in variable gene usage, gene pairing, somatic hypermutation, as well as the presence of convergent antiviral signatures across multiple individuals, including the presence of public antibody clonotypes. Notably, our results showed that, for B-cell receptors originating from different individuals but leveraging an identical combination of heavy and light chain variable genes, there is a specific CDRH3 identity threshold above which B cells appear to exclusively share the same antigen specificity. This finding provides a quantifiable measure of the relationship between antibody sequence and antigen specificity and further defines experimentally grounded criteria for defining public antibody clonality.IMPORTANCEThe B-cell compartment of the humoral immune system plays a critical role in the generation of antibodies upon new and repeated pathogen exposure. This study provides an unprecedented level of detail on the molecular characteristics of antibody repertoires that are specific to each of the different target pathogens studied here and provides empirical evidence in support of a 70% CDRH3 amino acid identity threshold in pairs of B cells encoded by identical IGHV:IGL(K)V genes, as a means of defining public clonality and therefore predicting B-cell antigen specificity in different individuals. This is of exceptional importance when leveraging public clonality as a method to annotate B-cell receptor data otherwise lacking antigen specificity information. Understanding the fundamental rules of antibody-antigen interactions can lead to transformative new approaches for the development of antibody therapeutics and vaccines against current and emerging viruses.

Residence time prediction in magnetically controlled biomolecular local rebinding-dissociation kinetics

The residence time of drug-target conjugates is a critical factor in drug screening and efficacy prediction. The local rebinding-dissociation kinetics gives insights into in-vivo drug-target interactions. A magnetic torque system (MTS) is designed to observe rebinding-dissociation kinetics for predicting residence time. The system utilizes an alternating magnetic field (AMF) to manipulate the magnetization motion of magnetically labeled biomolecules and the forces acting upon biomolecular bonds. The motion, sensed by a quartz crystal microbalance (QCM), reflects biomolecular interactions occurring at the particle surface. Meanwhile, the motion facilitates the separation of dissociated molecules from the surface, thereby obviating the necessity for fixed and mobile phases in common kinetics observations. The constant and static solution environment minimizes reagent consumption. The MTS was utilized to observe the local rebinding-dissociation of antibodies (PAB and MAB) to magnetic beads (MB) and to HER2 receptors. The residence times recorded by the MTS were larger than the results obtained via SPR method, due to the occurrences of rebinding-dissociation kinetics. Interaction behaviours can be meticulously regulated for varying affinities by modulating the intensity of magnetic field. A high intensity field (400 Oe) was applied for strong binding between antibody-MB (biotin-streptavidin), and a low intensity field (300 Oe) was applied for weak antigen-antibody interactions. An increase in AMF strength enhanced dissociation, with a shift from 300 Oe to 400 Oe resulting in a 1 ∼ 4-fold reduction in residence time. Overall, the MTS provides an interactive and customizable perspective on kinetics observations.

Chemically Self-Assembled Monolayer Semiconducting Single-Walled Carbon Nanotube-Based Biosensor Platform for Amyloid-β Detection.

This paper presents a platform for amyloid-β (Aβ) biosensors, employing nearly monolayer semiconducting single-walled carbon nanotubes (sc-SWNTs) via click reaction. A high-purity sc-SWNT ink was obtained by employing a conjugated polymer wrapping method with the addition of silica gel. Aβ detection involved monitoring the electrical resistances of the sc-SWNT layers. Electrical resistances increased rapidly corresponding to the concentration of amyloid-β 1-42 (Aβ1-42) peptides. Furthermore, we introduced Aβ peptides onto the 1-pyrenebutanoic acid succinimidyl ester (PBASE) linker, confirming that only the chemical adsorption of the peptide by the antibody-antigen reaction yielded a significant change in electrical resistance. The optimized sensor exhibited a high sensitivity of 29% for Aβ at a concentration of 10 pM. Notably, the biosensor platform featuring chemically immobilized sc-SWNT networks can be customized by incorporating various bioreceptors beyond Aβ antibodies.

Rational design based on multi-monomer simultaneous docking for epitope imprinting of SARS-CoV-2 spike protein

Among biomimetic strategies shaping engineering designs, molecularly imprinted polymer (MIP) technology stands out, involving chemically synthesised receptors emulating natural antigen-antibody interactions. These versatile ‘designer polymers’ with remarkable stability and low cost, are pivotal for in vitro diagnostics. Amid the recent global health crisis, we probed MIPs’ potential to capture SARS-CoV-2 virions. Large biotemplates complicate MIP design, influencing generated binding site specificity. To precisely structure recognition sites within polymers, we innovated an epitope imprinting method supplemented by in silico polymerization component screening. A viral surface Spike protein informed epitope selection was targeted for MIP development. A novel multi-monomer docking approach (MMSD) was employed to simulate classical receptor-ligand interactions, mimicking binding reinforcement across multiple amino acids. Around 40 monomer combinations were docked to the epitope sequence and top performers experimentally validated via rapid fluorescence binding assays. Notably, high imprinting factor polymers correlated with MMSD predictions, promising rational MIP design applicable to diverse viral pathologies.

Improved deep learning prediction of antigen–antibody interactions

Identifying antibodies that neutralize specific antigens is crucial for developing effective immunotherapies, but this task remains challenging for many target antigens. The rise of deep learning-based computational approaches presents a promising avenue to address this challenge. Here, we assess the performance of a deep learning approach through two benchmark tests aimed at predicting antibodies for the receptor-binding domain of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein. Three different strategies for constructing input sequence alignments are employed for predicting structural models of antigen-antibody complexes. In our initial testing set, which comprises known experimental structures, these strategies collectively yield a significant top-ranked prediction for 61% of cases and a success rate of 47%. Notably, one strategy that utilizes the sequences of known antigen binders outperforms the other two, achieving a precision of 90% in a subsequent test set of ~1,000 antibodies, balanced between true and control antibodies for the antigen, albeit with a lower recall of 25%. Our results underscore the potential of integrating deep learning methods with single B cell sequencing techniques to enhance the prediction accuracy of antigen-antibody interactions.

The Evolution of Illicit-Drug Detection: From Conventional Approaches to Cutting-Edge Immunosensors-A Comprehensive Review.

The increasing use of illicit drugs has become a major global concern. Illicit drugs interact with the brain and the body altering an individual's mood and behavior. As the substance-of-abuse (SOA) crisis continues to spread across the world, in order to reduce trafficking and unlawful activity, it is important to use point-of-care devices like biosensors. Currently, there are certain conventional detection methods, which include gas chromatography (GC), mass spectrometry (MS), surface ionization, surface-enhanced Raman spectroscopy (SERS), surface plasmon resonance (SPR), electrochemiluminescence (ECL), high-performance liquid chromatography (HPLC), etc., for the detection of abused drugs. These methods have the advantage of high accuracy and sensitivity but are generally laborious, expensive, and require trained operators, along with high sample requirements, and they are not suitable for on-site drug detection scenarios. As a result, there is an urgent need for point-of-care technologies for a variety of drugs that can replace conventional techniques, such as a biosensor, specifically an immunosensor. An immunosensor is an analytical device that integrates an antibody-based recognition element with a transducer to detect specific molecules (antigens). In an immunosensor, the highly selective antigen-antibody interaction is used to identify and quantify the target analyte. The binding event between the antibody and antigen is converted by the transducer into a measurable signal, such as electrical, optical, or electrochemical, which corresponds to the presence and concentration of the analyte in the sample. This paper provides a comprehensive overview of various illicit drugs, the conventional methods employed for their detection, and the advantages of immunosensors over conventional techniques. It highlights the critical need for on-site detection and explores emerging point-of-care testing methods. The paper also outlines future research goals in this field, emphasizing the potential of advanced technologies to enhance the accuracy, efficiency, and convenience of drug detection.

B-036 Systematic Evaluation of Antibody-Antigen Interactions for Suitability in Immunoassays Using Parallel Microarray ELISA

Abstract Background A critical step in immunoassay development is the selection of the appropriate capture and detection antibodies. The use of suboptimal antibodies can drastically affect the quality of the final assay. Traditional selection methods involve a direct testing of potential antibodies in the final assay setting; a time-consuming and material-intensive exercise that is often too complex to test all possibilities. Alternatives, such as abstract K-on/K-off determinations via SPR or BLI can be costly and often show limited translatability to the ultimate assay format. Here, we describe a novel systematic approach to rapidly screen antibody-antigen interactions for key assay specifications and test for translatability of the findings. Methods Nanoliter volumes of potential capture antibodies were spotted onto the bottom of ELISA wells, generating a microarray of antibodies in each well (>11,000 spots per plate). For the analysis of capture antibody-antigen interactions, a titration series of biotinylated analyte in negative sample matrix was applied. Binding was visualized using strep-polyHRP, precipitating TMB and high resolution scanning of each ELISA well. Antibody pair performance was investigated using unlabeled analyte and biotinylated detection antibody options. Sensitivity, specificity, dynamic range, binding speed, binding strength and matrix compatibility were investigated using time courses, concentration ranges, dilutions, interferent spikes and pH/salt/detergent challenges. Signals were quantified to calculate and statistically compare each possible interaction. Results Antibody-antigen interactions were investigated via parallel microarray ELISA for two example immunoassays: the HBsAg and cTnI test. For both analytes, a panel of antibodies was screened revealing antibodies with diverse characteristics. Striking differences were observed depending on the sample matrix composition. Without optimization, antibodies could be identified showing dynamic assay ranges of 3-4 log orders. Time course experiments revealed high-affinity antibodies suitable for rapid diagnostic tests. For HBsAg, levels down to 0.01 IU/mL (∼40 pg/mL) could be reproducibly detected, which is well below the minimal requirement of 0.13 IU/mL. Reactivity against multiple virus serotypes could be confirmed. Interestingly, for the cTnI test, a strong synergistic effect was observed when specific monoclonal antibodies were combined in the capture mix, resulting in a 6-fold sensitivity enhancement compared to their separate performances. Finally, we show that the results obtained with the parallel microarray ELISA approach are highly translatable to the final assay format, ranging from bead-based dry-chemistry assays to chemiluminescent immunoassays. Conclusions The parallel microarray ELISA evaluation presents a robust and reliable method for identifying the best antibodies for an immunoassay. It allows for a systematic and quantitative screen of numerous antibody options within 1-2 weeks under real immunoassay conditions with minimal antibody amounts. This method has shown to be highly translatable and is customizable to accommodate required assay specifications.

B-064 Bovine Serum Albumin Coats Silicon Dioxide More Uniformly Near its Isoelectric Point Based on Atomic Force Microscopy Analysis

Abstract Background Bovine serum albumin (BSA) serves as a blocking agent in in-vitro diagnostic (IVD) applications, with fatty acid stabilized (FAS) and fatty acid free (FAF) BSA as common options. The presence of fatty acids and pH influence the conformation of BSA. While previous studies have explored the individual effects of fatty acids or pH on BSA blocking, limited data exists on their combined impact. This study employs atomic force microscopy (AFM) to investigate how pH and fatty acids jointly affect BSA adsorption onto silica, a surface relevant within diagnostics. Methods AFM, a sensitive microscopic technique, provides topographic images of surfaces with superior resolution compared to optical microscopy. We conducted experiments to assess the adsorption of 1% FAS and FAF BSA on a silica surface at pH 5.2 and pH 7. Evaluation of the coatings involved scratching to assess overall thickness, and roughness, indicating thickness variability. Results Results revealed that both fatty acids and pH play significant roles in shaping the BSA coating characteristics on silica surfaces. In three out of four experimental conditions, the average thickness of the BSA coating ranged between 3-4 nm, except for FAS BSA at pH 7, which exhibited a thicker average coating measuring 5.6 nm. The pH impacted the roughness of the BSA coating. Coatings prepared at pH 5.2 displayed smoother surfaces compared to those at pH 7. Utilizing the root mean square average height variability analysis, there is 95% confidence that the FAF and FAS BSA surfaces at pH 5.2 had a height range between 2.4-4.3 nm and 2.0-4.9 nm, respectively, indicating complete coverage of the silica surface without much height variation. In contrast, the FAF and FAS BSA coatings at pH 7 exhibited a wider range of heights, with 95% confidence intervals spanning from -1.5-8.6 nm and 0.0-11.2 nm, respectively. This variability suggests potential issues including incomplete BSA coverage at the lower range, and steric hindrance at the higher end, highlighting the importance of pH in optimizing BSA coatings for various applications. Conclusions Silica is commonly used in IVD assays, with BSA frequently employed to coat and passivate the surface. Our AFM experiments highlight the impact of pH and fatty acid presence on coating thickness and uniformity. Notably, FAS BSA at pH 7 exhibited the thickest coating; however, its increased roughness may pose challenges in IVD applications due to steric hindrance blocking antibody-antigen interactions. Alternatively, FAF BSA at pH 7 displayed an adequate thickness but also considerable roughness, suggesting potential insufficiencies in surface coverage and passivation. FAS and FAF BSA coatings at pH 5.2 showed relatively consistent thickness, indicating a more uniform surface which may be ideal for IVD coating applications. These AFM experiments were conducted on pure silica surfaces, which may differ from surfaces engineered for IVD. Additionally, both FAF and FAS BSA grades are proven to be effective in IVD applications. Further research employing AFM and other biophysical techniques is warranted to deepen our understanding of pH and fatty acid effects on BSA-surface interactions, aiming to enhance blocking strategies in IVD.

Bimetallic Au-Ag Reduced Graphene Oxide Nanostructures for Enhanced Detection of Dengue Virus E Protein

The development of highly sensitive and specific diagnostic tools for early-stage detection of dengue virus (DENV) is critical for effective outbreak management, particularly in resource-limited settings. In this study, we report a novel electrochemical immunosensor based on bimetallic gold silver (Au-Ag) nanoparticles integrated with reduced graphene oxide (rGO) for the detection of dengue virus envelope (E) protein. The Au-Ag bimetallic nanostructures exhibit superior electron transfer kinetics and enhanced electrocatalytic activity, while rGO serves as an excellent platform due to its large surface area and high conductivity. This synergistic combination improves antigen-antibody interactions and significantly boosts sensor performance. The immunosensor demonstrated a broad linear detection range of 100 ag ml−1 to 10 ng ml−1, with a high correlation coefficient (R2 = 0.98519). It achieved an ultra-low limit of detection (LOD) of 4.959 ag ml−1 for DENV E protein, outperforming existing detection methods. These findings highlight the potential of the Au-Ag- rGO-based immunosensor as a promising tool for point-of-care diagnosis, enabling rapid and cost-effective disease management and control.

Preparation and preliminary application of fluorescent microsphere test strips for feline parvovirus antibodies

This study introduces a novel diagnostic modality for the detection of feline panleukopenia virus (FPV) antibodies in feline serum by using fluorescent microsphere immunochromatographic test strips (FM-ICTS). Leveraging the inherent specificity of antigen-antibody interactions, the FM-ICTS approach demonstrates considerable potential for efficient and accurate FPV antibody detection within a short timeframe. The FM-ICTS method demonstrates strong diagnostic performance, with consistent accuracy and stability over time. PBS buffer dilution enables detection across the range of FPV antibody haemagglutination inhibition (HI) titres in both healthy and immunized or infected cats. A high correlation (R² = 0.9733) between the T/C ratio and FPV antibody titres confirms the method’s effectiveness in quantifying these titres. Clinical validation with 84 samples supports its reliability by matching results with HI assays. Additionally, stability tests show that the test strips maintain performance during storage, with a coefficient of variation (CV) below 12% over three months at 25℃. This innovative FM-ICTS framework emerges as a promising avenue for expedient and dependable disease diagnosis within the realm of veterinary science, offering implications for timely disease management and surveillance.

Development and experimental validation of computational methods for human antibody affinity enhancement.

High affinity is crucial for the efficacy and specificity of antibody. Due to involving high-throughput screens, biological experiments for antibody affinity maturation are time-consuming and have a low success rate. Precise computational-assisted antibody design promises to accelerate this process, but there is still a lack of effective computational methods capable of pinpointing beneficial mutations within the complementarity-determining region (CDR) of antibodies. Moreover, random mutations often lead to challenges in antibody expression and immunogenicity. In this study, to enhance the affinity of a human antibody against avian influenza virus, a CDR library was constructed and evolutionary information was acquired through sequence alignment to restrict the mutation positions and types. Concurrently, a statistical potential methodology was developed based on amino acid interactions between antibodies and antigens to calculate potential affinity-enhanced antibodies, which were further subjected to molecular dynamics simulations. Subsequently, experimental validation confirmed that a point mutation enhancing 2.5-fold affinity was obtained from 10 designs, resulting in the antibody affinity of 2nM. A predictive model for antibody-antigen interactions based on the binding interface was also developed, achieving an Area Under the Curve (AUC) of 0.83 and a precision of 0.89 on the test set. Lastly, a novel approach involving combinations of affinity-enhancing mutations and an iterative mutation optimization scheme similar to the Monte Carlo method were proposed. This study presents computational methods that rapidly and accurately enhance antibody affinity, addressing issues related to antibody expression and immunogenicity.