Biosensors-Basel最新文献

The prevention and early warning of foot ulcers are crucial in diabetic care; however, early microvascular lesions are difficult to detect and often diagnosed at later stages, posing serious health risks. Infrared thermal imaging, as a rapid and non-contact clinical examination technology, can sensitively detect hidden neuropathy and vascular lesions for early intervention. This review provides an informative summary of the background, mechanisms, thermal image datasets, and processing techniques used in thermal imaging for warning of diabetic foot ulcers. It specifically focuses on two-dimensional signal processing methods and the evaluation of computer-aided diagnostic methods commonly used for diabetic foot ulcers.

Diagnostics often require specialized equipment and trained personnel in laboratory settings, creating a growing need for point-of-care tests (POCTs). Among the genetic testing methods available, Loop-mediated Isothermal Amplification (LAMP) offers a viable solution for developing genetic POCT due to its compatibility with simplified devices. This study aimed to create a genetic test that integrates all steps from sample processing to analyzing results while minimizing the complexity, handling, equipment, and time required. Several challenges were addressed to achieve this goal: (1) the development of a buffer for bacterial DNA extraction that is compatible with both LAMP and immunochromatographic tests; (2) the adaption of the LAMP protocol for use with the SPID device; and (3) the optimization of the detection protocol for specific test conditions, with a lateral flow immunoassay format selected for its POCT compatibility. Following these developments, the test was validated using Escherichia coli (E. coli) and non-E. coli strains. A portable heating station was also developed to enable amplification without costly equipment. The resulting genetic POCT achieved 100% sensitivity and 85% specificity, with results available in 60 to 75 min. This study demonstrated that our POCT efficiently performs DNA extraction, amplification, and detection for bacterial identification. The test's simplicity and cost-effectiveness will support its implementation in various settings.

Lateral flow assays are widely used in point-of-care diagnostics but face challenges in sensitivity and accuracy when detecting low analyte concentrations, such as thyroid-stimulating hormone biomarkers. This study aims to enhance assay performance by leveraging textural features and hybrid artificial intelligence models. A modified Gray-Level Co-occurrence Matrix, termed the Averaged Horizontal Multiple Offsets Gray-Level Co-occurrence Matrix, was utilised to compute the textural features of the biosensor assay images. Significant textural features were selected for further analysis. A deep learning Convolutional Neural Network model was employed to extract features from these textural features. Both traditional machine learning models and hybrid artificial intelligence models, which combine Convolutional Neural Network features with traditional algorithms, were used to categorise these textural features based on the thyroid-stimulating hormone concentration levels. The proposed method achieved accuracy levels exceeding 95%. This pioneering study highlights the utility of textural aspects of assay images for accurate predictive disease modelling, offering promising advancements in diagnostics and management within biomedical research.

Bright biocompatible fluorescent imaging dyes with red to near-infrared (NIR) emissions are ideal candidates for fluorescence microscopy applications. Pyrene-benzothiazolium hemicyanine dyes are a new class of lysosome-specific probes reported on recently. In this work, we conduct a detailed implementation study for a pyrene-benzothiazolium derivative, BTP, to explore its potential imaging applications in fluorescence microscopy. The optical properties of BTP are studied in intracellular environments through advanced fluorescence microscopy techniques, with BTP exhibiting a noticeable shift toward blue (λ_em ≈ 590 nm) emissions in cellular lysosomes. The averaged photon arrival time (AAT)-based studies exhibit two different emissive populations of photons, indicating the probe's dynamic equilibrium between two distinctively different lysosomal microenvironments. Here, BTP is successfully utilized for time-lapse fluorescence microscopy imaging in real-time as a 'wash-free' imaging dye with no observed background interference. BTP exhibits an excellent ability to highlight microorganisms (i.e., bacteria) such as Bacillus megaterium through fluorescence microscopy. BTP is found to be a promising candidate for two-photon fluorescence microscopy imaging. The two-photon excitability of BTP in COS-7 cells is studied, with the probe exhibiting an excitation maximum at λ_TP ≈ 905 nm.

Microfluidic devices have revolutionized biosensing by enabling precise manipulation of minute fluid volumes across diverse applications. This review investigates the incorporation of machine learning (ML) into the design, fabrication, and application of microfluidic biosensors, emphasizing how ML algorithms enhance performance by improving design accuracy, operational efficiency, and the management of complex diagnostic datasets. Integrating microfluidics with ML has fostered intelligent systems capable of automating experimental workflows, enabling real-time data analysis, and supporting informed decision-making. Recent advances in health diagnostics, environmental monitoring, and synthetic biology driven by ML are critically examined. This review highlights the transformative potential of ML-enhanced microfluidic systems, offering insights into the future trajectory of this rapidly evolving field.

Background: Nasopharyngeal carcinoma (NPC) is a malignant tumor with high prevalence in southern China. Aberrant DNA methylation, as a hallmark of cancer, is extensively present in NPC, the detection of which facilitates early diagnosis and prognostic improvement of NPC. Conventional methylation detection methods relying on bisulfite conversion have limitations such as time-consuming, complex processes and sample degradation; thus, a more rapid and efficient method is needed.

Methods: We propose a novel DNA methylation assay based on methylation-sensitive restriction endonuclease (MSRE) HhaI digestion and Glycerol-enhanced recombinase polymerase amplification (RPA)-CRISPR/Cas12a detection (HGRC). MSRE has a fast digestion rate, and HhaI specifically cleaves unmethylated DNA at a specific locus, leaving the methylated target intact to trigger the downstream RPA-Cas12a detection step, generating a fluorescence signal. Moreover, the detection step was supplemented with glycerol for the separation of Cas12a-containing components and RPA- and template-containing components, which avoids over-consumption of the template and, thus, enhances the amplification efficiency and detection sensitivity.

Results: The HGRC method exhibits excellent performance in the detection of a CNE2-specific methylation locus with a (limit of detection) LOD of 100 aM and a linear range of 100 aM to 100 fM. It also responds well to different methylation levels and is capable of distinguishing methylation levels as low as 0.1%. Moreover, this method can distinguish NPC cells from normal cells by detecting methylation in cellular genomes. This method provides a rapid and sensitive approach for NPC detection and also holds good application prospects for other cancers and diseases featuring DNA methylation as a biomarker.

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.

Simultaneous monitoring of antimicrobial responses to bacterial metabolic activity and biofilm formation is critical for efficient screening of new anti-biofilm drugs. A microbial fuel cell-based biosensor using Pseudomonas aeruginosa as an electricigen was constructed. The effects of silver nanoparticles (AgNPs) on the cellular metabolic activity and biofilm formation of P. aeruginosa in the biosensors were investigated and compared with the traditional biofilm detection method. The crystal violet staining results showed that the concentration of AgNPs being increased to 20 and 40 μg/mL had a slight and obvious inhibitory effect on biofilm formation, respectively. In comparison, the detection sensitivity of the biosensor was much higher. When the concentration of AgNPs was 5 μg/mL, the output voltage of the biosensor was suppressed, and the inhibition gradually increased with the AgNPs dose. AgNPs inhibited the activity of planktonic cells in the anolyte and the formation of biofilm on the anode surface, and it had a dose-dependent effect on the secretion of phenazine in the anolyte. The biosensor could monitor the impacts of AgNPs not only on biofilm formation but also on cell activity and metabolic activity. It provides a new and sensitive method for the screening of anti-biofilm drugs.

Escherichia coli (E. coli) detection suffers from slow analysis time and high costs, along with the need for specificity. While state-of-the-art electrochemical biosensors are cost-efficient and easy to implement, their sensitivity and analysis time still require improvement. In this work, we present a paper-based electrochemical biosensor utilizing magnetic core-shell Fe₂O₃@CdSe/ZnS quantum dots (MQDs) to achieve fast detection, low cost, and high sensitivity. Using electrochemical impedance spectroscopy (EIS) as the detection technique, the biosensor achieved a limit of detection of 2.7 × 10² CFU/mL for E. coli bacteria across a concentration range of 10²-10⁸ CFU/mL, with a relative standard deviation (RSD) of 3.5781%. From an optical perspective, as E. coli concentration increased steadily from 10⁴ to 10⁷ CFU/mL, quantum dot fluorescence showed over 60% lifetime quenching. This hybrid biosensor thus provides rapid, highly sensitive E. coli detection with a fast analysis time of 30 min. This study, which combines the detection advantages of electrochemical and optical biosensor systems in a graphite-based paper sensor for the first time, has the potential to meet the needs of point-of-care applications. It is thought that future studies that will aim to examine the performance of the production-optimized, portable, graphite-based sensor system on real food samples, environmental samples, and especially medical clinical samples will be promising.

Accurate and selective monitoring of thiamine levels in multivitamin supplements is essential for preventing deficiencies and ensuring product quality. To achieve this, a Förster resonance energy transfer (FRET) system using carbon dots (CDs) as energy donors and citrate-stabilized silver nanoparticles (AgNPs) as energy acceptors was developed. The aqueous synthesis of AgNPs using microwave irradiation was optimized to obtain efficient plasmonic nanoparticles for FRET applications, targeting maximal absorbance intensity, stability, and wavelength alignment. Using a central composite orthogonal design (CCOD), the optimal conditions were identified as a 12.5 min microwave reaction time, a Ag molar ratio of 0.72, and a pH of 8.28. The FRET sensing scheme was applied for thiamine determination, where the vitamin's presence impaired the FRET process, restoring CDs' photoluminescence (PL) emission in a concentration-dependent manner. To mitigate interference from other vitamins, PL kinetic data and excitation-emission matrix (EEM) data were analyzed using unfolded partial least-squares (U-PLS) with the subsequent application of the residual bilinearization technique (RBL), achieving high sensitivity and specificity for thiamine detection. This method demonstrated its accuracy and robustness by attaining a determination coefficient (R²) of 0.952 and a relative error of prediction (REP%) of 11%. This novel method offers highly sensitive and interference-free thiamine detection, with significant potential for a wide range of analytical applications.