Kahweol (KAH), a bioactive diterpene abundant in unfiltered coffee, has drawn growing pharmacological interest for its anticancer, anti-inflammatory, anti-angiogenic, and metabolic regulatory properties. However, sensitive and rapid methods for its determination in biological matrices remain scarce. This study developed a molecularly imprinted polymer (MIP)-based electrochemical sensor for selective KAH detection using a glassy carbon electrode modified with a sodium alginate-multi-walled carbon nanotube nanocomposite. The imprinted film was formed by electropolymerizing methacrylic acid (MA) with aniline (ANI) as a co-monomer around the KAH template, producing a poly(MA@ANI) recognition layer. Fabrication was optimized for nanocomposite composition, monomer-to-template ratio, polymerization cycles, template removal, and rebinding time. Under optimized conditions, differential pulse voltammetry (DPV) showed a linear range of 2.5 × 10–12 to 2.5 × 10–11 M, with a limit of detection (LOD) of 1.17 × 10–13 M and a quantification limit (LOQ) of 3.51 × 10–13 M. Selectivity tests, including imprinting factor calculations and competitive binding against caffeic acid, caffeine, and interferents, confirmed high specificity toward KAH. Recovery experiments in real rabbit serum gave an LOD of 2.88 × 10–13 M, and favorable AGREE, AGREE-MIP, and ComplexMoGAPI scores supported the method’s greenness. Computational modeling (MD, RDF, and DFT) supported the MIP design, revealing stable KAH–monomer complexation through strong hydrogen bonding. This is the first electrochemical determination of KAH in a biological matrix and offers a rapid, cost-effective, green alternative for bioanalytical use.
Dysregulated amino acid (AA) profiles are closely associated with diseases. Comprehensive and quantitative profiling of AA is therefore essential. This study developed a simple, direct, and high-throughput serum AA profiling method and evaluated its auxiliary diagnostic potential for type 2 diabetes mellitus (T2DM). A cold-induced phase separation strategy coupled with ultra-performance liquid chromatography–tandem mass spectrometry was developed, enabling comprehensive serum amino acid profiling of 27 AAs and related metabolites across branched-chain, aromatic, and tryptophan metabolic pathways. The optimized sample preparation used acetonitrile (ACN) at a serum-to-solvent ratio of 1:3, 50 min of cold-induced phase separation, and reconstitution in ACN/H2O (v/v, 1:4). The method showed good linearity (R2 ≥ 0.9929), limits of detection of 0.005–5.00 ng/mL, limits of quantification of 0.01–10.00 ng/mL, recoveries of 82.6
The detection of PIK3CA gene mutations holds significant clinical importance for precise targeted treatment of breast cancer. Mutational status is a key factor in predicting whether patients will derive clinical benefit from PI3Kα inhibitor treatment. In this study, a label-free electrochemical biosensor was constructed using a Co/CoFe2O4/C@Au magnetic nanocomposite. This nanocomposite, synthesized via anion exchange between ZIF-67 and [Fe(CN)6]3−, followed by carbothermal reduction and gold deposition, exhibits a unique hollow structure with uniform particle size, small dimensions, and a large specific surface area. Additionally, the uniform distribution of cobalt, carbon, and gold endows the material with excellent electrical conductivity and magnetism. After modifying this material onto the electrode, the target gene is directly captured at the sensing interface without enzymes or labeled probes, and the electrochemical signal is amplified by the nanocomposite. The electrochemical detection results for the PIK3CA gene showed a linear response range of 1 pM to 1 µM (R2 = 0.9992), a detection limit as low as 1.62 pM, and good selectivity for single-base mismatches. Spike-and-recovery tests in 10
Single-nucleotide variants (SNVs) are a critical molecular basis for antibiotic resistance in pathogenic bacteria. However, precise detection of mutant targets remains challenging because they may exist at low abundance and often coexist with wild-type sequences in complex biological matrices. Herein, we report a target-triggered strand displacement reaction that releases two functional DNA strands: one activates the trans-cleavage activity of CRISPR/Cas12a to generate a fluorescent signal, while the other competitively displaces cadmium sulfide quantum dots pre-modified on a gold electrode surface, decreasing the electrochemiluminescence (ECL) signal. The two functional strands hybridize to distinct regions of the probe, enabling the two signaling channels to respond differentially to single-base mismatches at different locations. Taking the response to wild-type targets as a reference, synchronized changes in both signals reflect target concentration. A significant decrease in the fluorescence signal alone, with the ECL signal unchanged, indicates that the mismatch occurs within the fluorescence recognition region, whereupon the unaffected ECL channel may serve for quantification. Conversely, when the ECL signal decreases while the fluorescence signal remains stable, the fluorescence channel may serve for quantification. This design enables a single probe to discriminate between wild-type and mutant targets based on the differential responses of the two signaling channels. Our strategy thus provides a new avenue for single-base nucleic acid detection.
Cyanobacteria are of global concern, impacting ecosystems and posing a public health risk from production of bioactive secondary metabolites, including several classes of potent mammalian toxins. Because of the thousands of possible metabolites, non-target analysis (NTA) using high-resolution mass spectrometry (HRMS) is of increasing interest for studying cyanobacteria. However, performance evaluation of these NTA methods is rarely considered, potentially raising concerns over the quality and validity of results. Here, a NTA workflow was developed for cyanobacterial metabolite profiling using data-dependent LC–HRMS/MS acquisition and semi-automated data processing, including product ion, mass list, and spectral library searching using metabolomics software. Rigorous method optimization and performance evaluation at each step were carried out based on detection of cyanotoxins in a multiclass cyanobacterial matrix reference material. Deeper profiling of this material further extended its utility for evaluation of NTA performance for metabolite classes beyond traditional cyanotoxins in the future. The workflow was applied to a set of environmental benthic cyanobacterial biofilm samples associated with recent animal deaths. This led to the detection of 116 secondary metabolites from microcystin, anatoxin, cyanopeptolin, anabaenopeptin, aeruginosin, and cyanobactin classes, greatly increasing reported compound diversity for benthic cyanobacteria, which have been poorly characterized compared to planktonic bloom-forming species. Because of the association of anatoxins in these samples with animal deaths, additional structural elucidation of two newly detected analogues was carried out via analytical scale semi-synthesis. The approaches presented are broadly applicable to class-based NTA in other fields where improved confidence in detection results is increasingly important.
