Sensors & diagnostics最新文献

Correction for ‘Highly selective detection of ethanol in biological fluids and alcoholic drinks using indium ethylenediamine functionalized graphene’ by Ramin Boroujerdi et al., Sens. Diagn., 2022, 1, 566–578, https://doi.org/10.1039/D2SD00011C.

Correction for ‘High resolution voltammetric and field-effect transistor readout of carbon fiber microelectrode biosensors’ by Whirang Cho et al., Sens. Diagn., 2022, 1, 460–464, https://doi.org/10.1039/D2SD00023G.

Correction for ‘Turn-on fluorescent sensors for Cu-rich amyloid β peptide aggregates’ by Yiran Huang et al., Sens. Diagn., 2022, 1, 709–713, https://doi.org/10.1039/D2SD00028H.

This work introduces a new electrochemical sensing approach, where the liquid sample containing nucleic acid targets can be blotted onto an electrode that is pre-functionalized with probe DNA. The post-hybridization signal and probe DNA signal (obtained by melting the hybrid) can be successively measured later, making the sensing scheme resilient to probe layer deterioration and circumventing the need to measure probe signal immediately before sample collection, ultimately mitigating the need for electrochemical sensing equipment at the sample collection site.

Unlike the polymerase chain reaction (PCR), loop-mediated isothermal amplification (LAMP) lacks a consistent thermal cycle, making quantification particularly challenging. Previously, we demonstrated that LAMP can accurately diagnose Kaposi sarcoma (KS) from skin lesion biopsies at the point of care (receiver operating characteristic area under the curve (AUC) = 0.967). A common approach in LAMP analysis involves setting a minimum absorbance threshold and time cutoff for positivity, which can introduce bias. We present a less biased, automated signal processing approach involving the fitting of a signal curve to five, two-parameter algebraic function fits, and the training of an artificial intelligence (AI) model on those parameters and their variances. An extreme gradient boosting (XGB) model was trained and tested on a primary dataset consisting of 1317 LAMP curves (from 451 unique patient samples with replicates). Five-fold k-validation on the train/test set yielded an receiver operating curve (ROC) area under the curve (AUC) of 0.952 ± 0.029. Each of the five-fold models were then validated on a separate secondary dataset of 966 LAMP curves (from 414 unique patient samples with replicates) and achieved an AUC of 0.950 ± 0.005. While the traditional methodology (which did not implement k-validation or a test/train split) outperformed the AI model's train/test set performance, the AI model generalized better and achieved a higher accuracy on the validation set (0.950 ± 0.005 vs. 0.9347). It performed even better when the analysis was applied directly to the raw signal data without additional pre-processing steps such as artifact filtering. This suggests that the AI model is more generalizable to new data and is able to discriminate KS-present and KS-absent samples better than traditional methods.

Biofilms are intricate bacterial communities encased in a self-produced extracellular matrix (ECM) of DNA, lipids, proteins, and polysaccharides. The diverse ECM composition across bacterial species significantly influences the progression of biofilm-associated infections, making precise identification crucial for effective treatment. Traditional methods such as biochemical assays, MALDI-TOF mass spectrometry, DNA sequencing and culturing provide valuable insights but have notable drawbacks, including time-consuming procedures, high costs, and the need for specialized equipment and trained personnel. These limitations hinder the rapid and widespread adoption of biofilm identification in clinical settings, underscoring the need for more streamlined, accurate, and accessible methods. In this study, we employed a paper-based ratiometric sensor array with fluorescent dyes (3-hydroxyflavone derivatives) pre-adsorbed onto paper microzone plates to identify bacterial biofilms. The fluorescence signals from the sensor upon interaction with biofilms were analyzed using linear discriminant analysis and different machine learning algorithms, including neural networks, support vector machines, and naïve Bayes classifiers. Our results show that the sensor array accurately distinguishes between biofilms of eight species with 97.5% classification accuracy. It effectively identifies individual bacteria at OD₆₀₀ as low as 0.002 o.u. Additionally, using neural networks, the sensor array achieves more than 95% accuracy in distinguishing planktonic bacteria from biofilms and shows over 85% accuracy in identifying clinical bacterial species and biofilms. These findings highlight the sensor's potential for high-precision biofilm identification in laboratory and clinical settings, offering a valuable tool for advancing biofilm research and enhancing clinical diagnostics.

In this research, a hierarchically structured, flower-like ZnO material was successfully synthesized via a solvothermal approach. Subsequently, silver (Ag) nanoparticles were deposited onto the ZnO flowers through ultraviolet light reduction, yielding a highly efficient Ag/ZnO composite material. Notably, the 3 at% Ag/ZnO composite demonstrated a remarkably enhanced response to 100 ppm H₂S at a relatively low operating temperature of 92 °C, reaching 430.0, which is significantly higher than the 157.3 observed for the pristine ZnO material. Furthermore, the detection limit for H₂S was dramatically lowered from 0.05 ppm to a mere 1 ppb. The findings of this research suggest that the incorporation of Ag nanoparticles substantially ameliorates the H₂S sensing capabilities of the pure ZnO material. To delve deeper into the underlying mechanisms, X-ray photoelectron spectroscopy (XPS) was utilized to explore the interaction between the Ag/ZnO sensor and H₂S gas. This analysis provided valuable insights into the reasons behind the observed enhancement in gas sensing performance, shedding light on the synergistic effects of the Ag nanoparticles and the ZnO matrix in the composite material.

The critical role of non-neuronal acetylcholine (ACh) as a biomarker, driving cancer proliferation and signaling neurodegenerative decline, demands sensitive, non-enzymatic diagnostic tools for early detection. This work presents a highly innovative non-enzymatic electrochemical sensor for the direct, ultra-sensitive quantification of ACh. The sensor is engineered by electropolymerizing a molecularly imprinted polypyrrole (MIP) matrix, embedded with uniquely structured ZnO·CuO nanoleaves (NLs), onto a disposable pencil graphite electrode. Computational modeling at the DFT level reveals strong non-covalent interactions that create high-fidelity recognition sites for ACh within the polymer. Comprehensive characterization (XRD, FTIR, FESEM, micro-CT, DLS) validates the successful synthesis of the nanocomposite and the precise formation of imprinting cavities. The optimized sensor achieves an exceptional detection limit of 2.2 pM and a broad linear dynamic range from 100 pM to 100 mM, ranking it among the most sensitive ACh sensors reported to date. It exhibits outstanding selectivity against key interferents and reliably detects ACh in human serum samples with excellent recovery (98.0–102.2%). This highly sensitive, robust, and cost-effective MIP-ZnO·CuO NL platform demonstrates immense potential for point-of-care clinical diagnostics in oncology and neurology.

Antimicrobial resistance (AMR) poses an urgent global health threat, driving the need for rapid and accurate antimicrobial susceptibility testing (AST). Traditional phenotypic AST methods remain the clinical gold standard but are hindered by prolonged turnaround times and labor-intensive procedures. Microfluidic technologies have emerged as transformative platforms, enabling miniaturized, high-throughput, and integrated phenotypic AST workflows with accelerated result delivery. This review comprehensively summarizes recent advances in microfluidic phenotypic AST, categorizing platforms by cultivation strategies—such as static chambers, flow chambers, SlipChip variants, and hybrid droplet-chamber systems—and surveying diverse signal detection modalities including fluorescence, label-free imaging, Raman, electrical, and mechanical readouts, each offering distinct advantages and limitations. Key innovations such as concentration gradient generation, digital single-cell manipulation, and AI-enhanced image analysis have significantly improved sensitivity, speed, and clinical applicability. However, widespread adoption remains challenged by sample-to-result integration, slow-growing pathogens, interference from residual antibiotics, and the lack of robust standardization. We further discuss emerging solutions, including automated sample preparation, multimodal detection, and computational data fusion, and outline future opportunities for translating microfluidic phenotypic AST into routine diagnostics. Collectively, these advances hold substantial promise for combating AMR by enabling personalized, rapid, and actionable antimicrobial therapy.

Microfluidic technology can expedite nucleic acid testing by converting the functions of bulky laboratory instruments and protracted bench methodologies into easy-to-use and inexpensive miniaturised systems without compromising speed and reliability. We developed a lab-on-a-chip (LOC) platform that integrates a dimethyl adipimidate (DMA)-based functionalised silica DNA isolation and pre-concentration method with a rapid and real-time loop-mediated isothermal amplification (LAMP) for detecting domoic acid-producing phytoplankton, Pseudo-nitzschia. An optimised design of a lab on a chip extraction module achieved a maximum DNA capture capacity of 61.73 ± 0.98 ng μL⁻¹. The DMA-based method reduced reagent costs per sample by 97% compared to a commercial nucleic acid isolation kit. A subsequent on-chip LAMP process was capable of sensitively quantifying cytochrome P450 homologous to the dabD gene, coding for a component of the domoic acid toxin production pathway, with a limit-of-detection of 10 cells per mL. LAMP-based detection of the target gene was achieved using dry-preserved reagents with a shelf-life of five months without refrigeration. There was no significant difference in assay performance between the preserved LAMP and freshly prepared LAMP mixtures. The total analysis time at the LOD of 10 cells per mL, from sample to result, was achieved within one hour. Our results demonstrate the long-term stability of assay reagents, rapid turnaround, and cost-effectiveness, offering a simple and economical approach to environmental monitoring and environmental bio-hazard diagnostics.