Biosensors

Myocardial infarction (MI) is a life-threatening cardiovascular disorder caused by a partial or complete interruption of oxygenated blood flow to the myocardium, leading to high mortality rates if not diagnosed promptly. Although electrocardiogram (ECG) signals are widely used due to their non-invasive and low-cost nature, MI-specific abnormalities may be subtle and subject to inter-observer variability. Therefore, reliable artificial intelligence-based decision support systems are essential to enhance diagnostic classification accuracy. In this study, only the Lead II derivation from 12-lead ECG recordings of 52 healthy individuals and 148 MI patients was analyzed. To effectively characterize the non-stationary nature of ECG signals, a hybrid time–frequency feature extraction framework was employed. Five-level intrinsic mode functions and wavelet detail and approximation coefficients were obtained using Empirical Mode Decomposition and Discrete Wavelet Transform with a Daubechies-6 wavelet. From these components, 390 times, nonlinear and complexity-based features were extracted using 23 entropy-driven measures. Particle Swarm Optimization was applied to select the most discriminative feature subset, significantly enhancing classification performance. The optimized features were evaluated using Support Vector Machines, Artificial Neural Networks, k-Nearest Neighbors, and Bagged Tree classifiers. The Bagged Trees classifier achieved the best classification performance with an overall correct classification rate of 97.6%. The results demonstrate that the proposed hybrid feature representation combined with PSO-based selection provides a robust and reliable framework for MI detection, offering strong potential for clinical decision support applications.

N-arachidonoylethanolamine (AEA) and 2-arachidonoylglycerol (2-AG) are lipid signalling molecules within the endocannabinoid system, which regulates numerous physiological processes and is implicated in diverse pathological conditions. Given the limited feasibility of obtaining human tissue samples, quantifying AEA and 2-AG in biological matrices is essential for understanding the endocannabinoid system in humans. While many studies have used blood samples for this purpose, the collection of this matrix typically requires invasive venipuncture, which limits the scalability and practicality of endocannabinoid research. This study validated extraction and LC–MS/MS methods for quantifying AEA and 2-AG (co-quantified with its isomer 1-AG) in minimally invasive matrices, including saliva and finger-prick blood microsamples, with acceptable linearity, recovery, reproducibility, and matrix effects. The assay additionally enabled exploratory quantification of arachidonic acid, oleoylethanolamide (OEA), palmitoylethanolamide (PEA), and selected steroid hormones, supporting multiplexed assessment from a single sample. Analyte concentrations measured in blood microsamples did not directly correspond to plasma concentrations, indicating that microsampling is suited for assessing relative within-study changes rather than absolute plasma equivalence. Application of the method demonstrated that venipuncture did not significantly alter salivary AEA or 2-AG concentrations. Overall, this method provides a minimally invasive and accessible approach for investigating endocannabinoid dynamics alongside other physiological biomarkers.

The manuscript describes an optical sensing microplate for the high-throughput screening of imidazolinone herbicides in soil extracts. As far as we know, imprinted proteins (IPs) specific to imidazolinone herbicides have not been synthesized and used as a recognition element for their solid-phase extraction before. Imprinted bovine serum albumin (BSA) and glucose oxidase (GOx) were synthesized in the presence of imazamox as a template and then these IPs were immobilized at the bottom of microplate wells. The sorption capacity (Q) of aminated silica nanoparticles modified by IPs (IP–BIS) was 6.38 mg g⁻¹ while the imprinting factor ($IF$) equaled 2.6. The concentration of imazamox was determined by a “turn-off” solid-phase assay using alloyed CdZnSeS/ZnS quantum dots (QDs) as a component of fluorescent substrate. Alloyed CdZnSeS/ZnS QDs were stabilized in an aqueous phase by positively charged cysteamine that, as far we know, had not been used as this type of ligand before. Our method allows for determining the concentration of imazamox in the range of 0.5–9.2 μg mL⁻¹, with a limit of quantification limit of quantitation (LOQ) equal to 0.45 μg mL⁻¹ The sensing microplate enables parallel detection of up to 96 samples containing herbicides using standard fluorescence microplate readers or smartphones. The paper describes how such sensing microplates can be used for the analysis of artificially contaminated soil samples. The proposed approach combines pre-concentration of analyte at the IPs with its subsequent determination on a single analytical platform, thus allowing for both highly sensitive determination in laboratory conditions and mass screening in the field.