Express and highly sensitive immunoassays for the quantitative registration of cardiac troponin I (cTnI) are in high demand for early point-of-care differential diagnosis of acute myocardial infarction. The selection of antibodies that feature rapid and tight binding with antigens is crucial for immunoassay rate and sensitivity. A method is presented for the selection of the most promising clones for advanced immunoassays via simultaneous characterization of interaction kinetics of different monoclonal antibodies (mAb) using a direct label-free method of multiplex spectral correlation interferometry. mAb-cTnI interactions were real-time registered on an epoxy-modified microarray glass sensor chip that did not require activation. The covalent immobilization of mAb microdots on its surface provided versatility, convenience, and virtually unlimited multiplexing potential. The kinetics of tracer antibody interaction with the “cTnI—capture antibody” complex was characterized. Algorithms are shown for excluding mutual competition of the tracer/capture antibodies and selecting the optimal pairs for different assay formats. Using the selected mAbs, a lateral flow assay was developed for rapid quantitative cTnI determination based on electronic detection of functionalized magnetic nanoparticles applied as labels (detection limit—0.08 ng/mL, dynamic range > 3 orders). The method can be extended to other molecular biomarkers for high-throughput screening of mAbs and rational development of immunoassays.

A novel approach to developing lateral flow assays (LFAs) for the detection of CYFRA 21-1 (cytokeratin 19 fragment, a molecular biomarker for epithelial-origin cancers) is proposed. Magnetic bioconjugates (MBCs) were employed in combination with advanced optical and magnetic tools to optimize assay conditions. The approach integrates such techniques as label-free spectral-phase interferometry, colorimetric detection, and ultrasensitive magnetometry using the magnetic particle quantification (MPQ) technique. For the first time in LFA applications, the MPQ-based and colorimetry-based detection methods were compared side by side, and superior analytical performance was demonstrated. The limit of detection (LOD) of 0.9 pg/mL was achieved using MPQ, and 2.9 pg/mL with optical detection. This study has demonstrated that MPQ provides elimination of signal saturation, higher sensitivity (slope of the calibration curve), and a 19-fold wider dynamic range of detected signals. Both optical and magnetic detection results are comparable to the best laboratory-based tests with the added benefits of a 20-min assay duration and the LFA format convenience. The assay effectiveness was validated in human serum and artificial saliva, and high recovery rates were observed. The proposed approach offers rapid and reliable detection of molecular biomarkers and holds significant potential for point-of-care diagnostics, particularly in resource-limited settings.

The study discloses the application of transformer-based deep learning models for the task of super-enhancers prediction in human tumor cell lines with a specific focus on sequence-only features within studied entities of super-enhancer and enhancer elements in the human genome. The proposed SE-prediction method included the GENA-LM application at handling long DNA sequences with the classification task, distinguishing super-enhancers from enhancers using H3K36me, H3K4me1, H3K4me3 and H3K27ac landscape datasets from HeLa, HEK293, H2171, Jurkat, K562, MM1S and U87 cell lines. The model was fine-tuned on relevant sequence data, allowing for the analysis of extended genomic sequences without the need for epigenetic markers as proposed in early approaches. The study achieved balanced accuracy metrics, surpassing previous models like SENet, particularly in HEK293 and K562 cell lines. Also, it was shown that super-enhancers frequently co-localize with epigenetic marks such as H3K4me3 and H3K27ac. Therefore, the attention mechanism of the model provided insights into the sequence features contributing to SE classification, indicating a correlation between sequence-only features and mentioned epigenetic landscapes. These findings support the potential transformer models use in further genomic sequence analysis for bioinformatics applications in enhancer/super-enhancer characterization and gene regulation studies.

In this study, we present a novel and ultrasensitive magnetic lateral flow immunoassay (LFIA) tailored for the precise detection of zearalenone, a mycotoxin with significant implications for human and animal health. A versatile and straightforward method for creating non-covalent magnetic labels is proposed and comprehensively compared with a covalent immobilization strategy. We employ the magnetic particle quantification (MPQ) technique for precise detection of the labels and characterization of their functionality, including measuring the antibody sorption density on the particle surface. Through kinetic studies using the label-free spectral phase interferometry, the rate and equilibrium constants for the binding of monoclonal antibodies with free (not bound with carrier protein) zearalenone were determined to be k_on = 3.42 × 10⁵ M⁻¹s⁻¹, k_off = 7.05 × 10⁻⁴ s⁻¹, and K_D = 2.06 × 10⁻⁹ M. The proposed MPQ-LFIA method exhibits detection limits of 2.3 pg/mL and 7.6 pg/mL when employing magnetic labels based on covalent immobilization and non-covalent sorption, with dynamic ranges of 5.5 and 5 orders, correspondingly. We have successfully demonstrated the effective determination of zearalenone in barley flour samples contaminated with Fusarium graminearum. The ease of use and effectiveness of developed test systems further enhances their value as practical tools for addressing mycotoxin contamination challenges.