Sensors & diagnostics最新文献

Cholesterol, a sterol lipid, is vital for various biological phenomena encompassing metabolism and cell functioning. Nevertheless, drastic changes in cholesterol levels will lead to severe cardiovascular disorders. The development of point-of-care technology plays a prominent role in frequent and pinpoint monitoring of cholesterol changes. The introduction of enzymatic biosensors revolutionized cholesterol detection; however, these sensors face significant challenges, including restricted stability, high expense, and sensitivity to environmental conditions. This review highlights the advancements in non-enzymatic electrochemical cholesterol biosensors, focusing on the application of novel materials, including metals and metal oxides, carbon and graphene-based materials, polymeric materials, MOFs, MXenes, photoelectrochemical materials, and advanced materials and composites, to enhance sensitivity, selectivity, and stability. Particular emphasis is placed on electrochemical techniques, material modifications, and their influence on sensing performance. For ease of comprehension and evaluation, standard statistics have been presented in a tabular format. Despite significant advancements, challenges such as miniaturization, reproducibility, and real-sample analysis persist. This review underscores the potential of nonenzymatic electrochemical biosensors to transform biosensing diagnostics and emphasizes the need for continued innovation in materials science and device integration.

Prostate cancer is one of the most common cancers in men, with the PSA (prostate-specific antigen) test serving as a cornerstone for its monitoring and early detection. This study describes the development and evaluation of an innovative quantitative lateral flow assay (LFA) utilizing luminescence from Bright-Dtech™ lanthanide nanoparticles to enhance the sensitivity and accuracy of PSA measurement. The optimized LFA demonstrated high sensitivity and reproducibility, with a detection limit of 15 pg mL⁻¹ in buffer (120 pg mL⁻¹ in 1 : 8 diluted serum), and a quantifiable range of 0.155 to 27.5 ng mL⁻¹ in buffer (1.24 to 221 ng mL⁻¹ in 1 : 8 diluted serum). This method was successfully applied for PSA detection in clinical serum samples, and it showed excellent correlation with a quantitative diagnostic reference method. The developed LFA offers a significant advancement in quantitative PSA testing, providing a rapid and cost-effective in vitro diagnostic solution. Furthermore, it showcases the potential of Bright-Dtech™ technology in lateral flow test design. With exceptional brightness and long luminescence lifetime, lanthanide nanoparticles effectively address key challenges in LFA sensitivity and quantification, paving the way for broader applications in diagnostic testing.

Traditional enzyme-linked immunosorbent assays (ELISAs) rely on horseradish peroxidase (HRP)-conjugated antibodies to generate a colorimetric response proportional to target antibody concentration. However, spectrophotometric quantification requires expensive benchtop equipment, limiting its usability for frequent, population-scale immunity screening. To overcome this barrier, we previously developed LC15, an antibody–invertase fusion protein that catalyzes sucrose-to-glucose conversion in proportion to antibody levels. This fusion protein enabled antibody quantification using handheld glucometers – affordable, widely available devices already integrated with telehealth infrastructure. Unlike commercial ELISAs, which report relative antibody titers, LC15 facilitates absolute antibody quantification (μg mL⁻¹), enhancing applications such as epidemiological monitoring and convalescent plasma dosing. To increase the number of clinical samples processed in a single run of the assay, in this study we transitioned from poly(methyl methacrylate) strips to microwell plates, optimizing pH conditions and reagent concentrations. This adaptation yielded similar sensitivity to the original strip-based assay, but with a 5-fold reduction in reagent consumption and in plasma, as opposed to serum used for the previous study. Using the SARS-CoV-2 receptor binding domain (RBD) as the antigen, we applied LC15 in a 96-well plate format to screen 72 clinical samples in triplicate for anti-RBD antibodies. A blinded comparison with commercial ELISAs demonstrated strong linear correlation (R² = 0.85) over four orders of magnitude in concentration. By combining accuracy with accessibility, this approach has the potential to facilitate population-level immunity assessments, supporting rapid public health responses in future outbreaks.

We developed a simple yet innovative biosensing system for the detection of miRNA-155 (miR-155), a promising biomarker for the early diagnosis of breast cancer. This system utilizes two types of gold nanoparticles (Au NPs) with opposing surface charges: (1) negatively charged citrate-stabilized Au NPs (Cit-Au NPs) for probe immobilization and (2) positively charged polyethylenimine-capped Au NPs (PEI-Au NPs) for signal amplification. The DNA probe was covalently attached to Cit-Au NPs via Au–S bonds. In the presence of miR-155, a DNA–miRNA hybrid forms, stabilizing the nanoparticles. The subsequent introduction of PEI-Au NPs enhances the surface plasmon resonance (SPR) signal due to increased nanoparticle dispersion. PEI-Au NPs enhance the diagnostic system's sensitivity by providing a high surface area and improved nanoparticle stability. Upon binding to the DNA–miRNA hybrid, the increased interparticle distance leads to enhanced colloidal stability. This stabilization manifests visually as an intensified red color, indicating the presence of the target when PEI-Au NPs are introduced into the solution. In contrast, in the absence of miR-155, electrostatic interactions cause aggregation of the Au NPs, leading to a measurable SPR shift. This facile method demonstrated a detection limit of approximately 8 pM and a wide linear detection range from 80 pM to 2 μM, making it a promising tool for early diagnostics of breast cancer.

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MYCN gene amplification critically drives neuroblastoma aggressiveness and poor outcomes, necessitating precise preoperative identification to guide risk-adapted therapies. Current invasive detection methods present substantial challenges for pediatric patients. To address this unmet need, we developed a noninvasive MRI-based radiomic signature for predicting MYCN amplification status in childhood abdominal neuroblastoma. In this prospective study, 99 patients with pathologically confirmed abdominal neuroblastoma underwent preoperative MRI between April 2019 and September 2021. From T2-weighted images, 1409 radiomic features were extracted per subject. Through two-sample statistical testing and least absolute shrinkage and selection operator (LASSO) regression, we constructed an optimized radiomic signature incorporating six highly discriminative features. The signature achieved exceptional performance (AUC = 0.91) in predicting MYCN amplification, significantly outperforming neuron-specific enolase levels (AUC = 0.68, p-value < 0.001) and all individual radiomic features. When integrated with neuron-specific enolase via multivariate logistic regression, the model achieved comparable performance (AUC = 0.91) to the signature only. Our findings establish the clinical viability of this MRI-based approach for noninvasively stratifying MYCN amplification status, offering significant potential to optimize surgical planning and therapeutic strategies for pediatric neuroblastoma.

Artificial intelligence (AI) is increasingly shaping modern healthcare by improving the accuracy and efficiency of disease diagnosis. This review summarises the modern advancements in AI-driven diagnostic technologies, with a focus on machine learning (ML) and deep learning (DL) applications for the detection and characterization of cancer, cardiovascular diseases, diabetes, neurodegenerative disorders, and bone diseases. AI models, particularly those employing convolutional neural networks, have demonstrated expert-level performances in interpreting medical images, genomic profiles, and electronic health records, often surpassing traditional diagnostic methods in terms of sensitivity, specificity, and overall accuracy. Using advanced methods like machine learning and deep learning, AI systems can analyze large and complex medical datasets—including images, electronic health records, and laboratory results—to detect patterns linked to various diseases. While integration of AI into clinical practice has shown significant benefits, challenges remain in ensuring the reliability, interpretability, and broad adoption of these systems. Thus, continued research and careful implementation are needed to maximize the potential of AI in transforming diagnostic processes and improving patient outcomes.

Cortisol is a hormone which regulates the body's response to stressors. Detection and monitoring of cortisol levels can provide information about physical and psychological health, thus it is essential to develop a sensor that can detect it in a sensitive manner. This study presents a biocompatible near-infrared fluorescent sensor, wherein single-walled carbon nanotubes (SWCNT) are functionalized with a cortisol-specific aptamer. We found this sensor was capable of detecting cortisol from 37.5 μg mL⁻¹ to 300 μg mL⁻¹ and that it was selective for cortisol compared to the similar molecule estrogen. Moreover, SWCNT functionalized with non-specific oligonucleotides did not exhibit a concentration-dependent response to cortisol, demonstrating the specificity provided by the aptamer sequence. The sensor also demonstrated the ability to detect cortisol in artificial cerebrospinal fluid. We anticipate that future optimization of this sensor will enable potential point-of-care or implantable device-based rapid detection of cortisol, with the potential for improving overall patient health and stress.

Rapid and accurate detection and characterization of pathogenic bacteria is critical for clinical diagnosis. Most selective clinical procedures are limited by their diagnostic speed, accuracy, and sensitivity challenges. In order to overcome these, we introduce a novel photonics-based, point-of-care device designed for rapid and accurate characterization of bacteria. The device is designed to capture optical scatter signatures unique to various pathogenic bacteria, which are analyzed using advanced clustering and machine learning techniques for characterization. Our preliminary results from controlled experiments show that our device successfully distinguishes bacteria genus with reasonable accuracy.

In the present study, we report for the first time a liquid crystal-based biosensor for the highly sensitive and specific detection of colon and breast cancer cells using folic acid-conjugated gold nanoparticles (FA@GNPs) as the recognition element. FA@GNPs were immobilized on a glass substrate coated with N-dimethyl-N-octadecyl-3-aminopropyltrimethoxysilyl chloride (DMOAP), which induces homeotropic alignment of the liquid crystal molecules. Folate receptors, which are overexpressed in various cancer types, including colon and breast cancer cells, facilitate the selective binding of these cells to FA@GNPs. This binding event disrupts the vertical alignment of the liquid crystal molecules, causing a distinct transition from a dark to a bright state, which is observable via polarizing optical microscopy. Quantitative analysis of the cancer cells was performed by calculating the average grayscale intensity of the optical images, demonstrating that the proposed cell detection platform can sensitively detect individual cancer cells. The proposed liquid crystal biosensor utilizing FA@GNPs as the detection element offers a simple, cost-effective, label-free platform with exceptional specificity and sensitivity for early cancer detection. This novel approach holds significant potential for the development of advanced diagnostic tools in oncological research.