Sensors & diagnostics最新文献

Detecting antibodies (Abs) is essential for assessing acquired immunity to infectious diseases, particularly following vaccination or prior infection. However, conventional serological tests often suffer from several limitations, including labor-intensive preparation, the need for specialized biosafety facilities, and lengthy processing times. Moreover, they provide only qualitative results with limited specificity. While homogeneous serological assays offer a simpler approach to detect Abs in biological samples, their sensitivity is often compromised by high background interference. In this study, we present a time-resolved fluorescence energy transfer (TR-FRET) assay for detecting infectious disease Abs in human sera. Our assay demonstrates high sensitivity in distinguishing between an antigen and its specific antibody, with no detectable upper limit of detection or prozone effect. It is universally applicable to various antigen–antibody complexes including SARS-CoV-2 and influenza, delivers accurate results within 15 minutes, and effectively mitigates background noise from human specimens. The development of this highly accurate immunoassay will enhance serological testing, making it faster, more reliable, and suitable for point-of-care settings.

Lab-on-a-chip (LoC) devices represent systems where microfluidics converges with state-of-the-art technologies, playing an immense role in reshaping clinical and biomedical sciences. This review deeply explores the design principles and diverse applications of LoC devices, ranging from point-of-care diagnostics to entire human-on-a-chip devices. Notably, LoC devices showcase remarkable adaptability and versatility. While LoC devices offer many advantages over conventional laboratory assessment methodologies including small sample size, reduced assay time and cost-effectiveness, the field faces many challenges in terms of designing, standardizing and large-scale production of the devices. In the end, while shedding light on how LoC devices stand at the forefront of the innovative technologies in the field of clinical and biomedical sciences, the review also emphasizes on their applications and integration with state-of-the-art technologies like AI and machine learning, along with their limitations and the further necessary developments for their widespread acceptance.

Theranostic clinical translation has been hindered by the lack of analytical tools facilitating interrogation. Here we report the detection of a chemotherapeutic agent, gemcitabine, released via heat trigger, from a hybrid iron oxide–gold theranostic nanoparticle surface in vitro in pancreatic cancer cells by electrochemiluminescence directly addressing this analytical gap.

The specific detection of Cu²⁺ and BF₃ provided the basis for the design of the distinctive dual-sensing chemosensor, 2-(benzo[d]thiazol-2-yl)-6-(1,4,5-triphenyl-1H-imidazol-2-yl) phenol (SP26). SP26 was synthesized successfully using a multi-step process, with its identity confirmed by NMR spectroscopy and HR-MS analysis. The studies were conducted in an 8 : 2 THF/water mixture. The ligand was solubilized in THF/water, whereas the cation salts were dissolved in water. The absorption measurements indicated no detection of cations other than Cu²⁺. The emission experiments revealed that the optical selectivity for the Cu²⁺ ion leads to a reduction in emission intensity. Likewise, with BF₃, the emission intensity diminishes with the bathochromic shift. The limit of detection (LoD) for Cu²⁺ is 381 pM, and for BF₃ it is 307 pM. After adding BF₃ and Cu²⁺ to SP26, the complex formation was so quick that it happened within a fraction of a second. Triethylamine (TEA) was used for BF₃, and ethylenediamine tetraacetic acid (EDTA) for Cu²⁺ to determine the reversibility. FT-IR, HR-MS, Job's plot, DFT, and ¹H NMR titration analyses confirmed that chemosensor SP26 was bound to Cu²⁺ and BF₃. Paper test strips showed the potential of the chemosensor SP26 for the environmental detection of Cu²⁺ and BF₃. The quantitative analysis of Cu²⁺ was examined with environmental water samples.

The development of reliable and cost-effective electrochemical sensors for hydrogen peroxide (H₂O₂) monitoring is crucial in biomedical diagnostics, especially in early disease diagnosis. Herein, we prudently synthesized an acid-functionalized COOH–Ti₃C₂T_x MXene, onto which a toluidine blue (TB) redox mediator was covalently immobilized and employed for the distinctive determination of H₂O₂. The synthesized COOH–Ti₃C₂T_x MXene is coated over a glassy carbon electrode (GCE), followed by the covalent immobilization of the electroactive TB dye through the N-(3-dimethylaminopropyl)-N′-ethylcarbodiimide hydrochloride (EDC)/N-hydroxysuccinimide (NHS) coupling reaction. This in turn results in the firm anchoring of the TB dye by establishing a stable amide linkage between the –COOH group of COOH–Ti₃C₂T_x and the free –NH₂ group of TB. Thus, the obtained TB/COOH–Ti₃C₂T_x/GCE sensor demonstrates an excellent electrocatalytic response for H₂O₂ determination over a broad linear range of 5 μM to 100 μM and 100 μM to 1.1 mM with a high sensitivity of 0.61 μA μM⁻¹ cm⁻² and a low detection limit of 1.5 μM. Notably, the fabricated electrode demonstrated exceptional stability and reproducibility as well as high selectivity and sensitivity in the detection of H₂O₂. Furthermore, the developed sensor showed very good recovery towards the detection of H₂O₂ in milk and serum samples. The attained analytical performance is attributed to the improved electrical wiring between the TB mediator and the conductive MXene platform.

Cystic fibrosis (CF) arises from mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene. Monitoring I⁻ transport serves as a critical approach for evaluating CFTR function in live cells, providing a foundation for the development of diagnostic tools and therapeutic treatments. Here, we report an iridium(III) complex (I-Sense) for the selective and pH-independent imaging of intracellular I⁻. By tracking cellular iodide I⁻ uptake, I-Sense facilitates the evaluation of CFTR activity in live cells, providing a valuable tool for the functional characterization of CFTR activity.

Organic thin-film transistors (OTFTs) have emerged as a promising platform for gas sensing applications due to their low-power operation, room-temperature sensitivity, and structural tunability. In this work, we investigate the effect of gate voltage (V_GS) on the ammonia (NH₃) sensing performance of OTFT-based sensors using copper phthalocyanine (CuPc, p-type) and its fluorinated derivative (F₁₆CuPc, n-type) as the active layers for the first time. Devices were exposed to NH₃ concentrations ranging from 0 to 100 ppm, and their electrical responses were monitored across different V_GS values. Results demonstrate that modulating V_GS significantly impacts key sensing parameters, including relative response (RR), sensitivity, limit of detection (LOD), and response/recovery kinetics. The lowest LODs achieved were 0.4 ppm for CuPc and 0.21 ppm for F₁₆CuPc. These findings highlight the potential of V_GS modulation as a powerful strategy to optimize OTFT sensor performance and provide a new dimension of tunability for gas detection technologies at room temperature.

Milk adulteration remains a significant public health concern in India, where conventional laboratory-based detection methods are often costly, time-consuming, and impractical for field use. This study introduces a novel paper-based microfluidic device designed for rapid, low-cost detection of multiple milk adulterants. The device comprises a 3D-printed strip holder and utilizes gravity-assisted capillary flow through porous paper, eliminating the need for hydrophobic barriers or external power sources. Its modular design allows for easy reuse of the holder while only replacing the paper strip for successive tests. The platform enables visual detection of common adulterants—including neutralizers, starch, hydrogen peroxide, urea, detergents, and boric acid—via reagent-specific colorimetric responses. The device meets the ASSURED criteria of World Health Organization for point-of-care diagnostics, offering a promising tool for decentralized milk quality monitoring and contributing to both consumer safety and improved supply chain transparency in the dairy industry. The device demonstrated a limit of detection (LOD) as low as 0.03% for urea and hydrogen peroxide, outperforming existing paper-based methods. The results were validated across five independent trials per condition, with high reproducibility and minimal cross-reactivity, confirming the diagnostic reliability of the platform.

Iron, particularly redox-active ferrous ions (Fe²⁺), is essential for biological processes. Despite their pivotal roles, analysis of Fe²⁺ ions within individual extracellular vesicles (EVs) has been hindered by the ultralow Fe²⁺ content and substantial heterogeneity of EVs. To address this, we developed a novel approach by integrating an Fe²⁺-specific fluorescent chemosensor (Ac-FluNox) with nano-flow cytometry (nFCM) for precise single-EV Fe²⁺ mapping. Method specificity to Fe²⁺ was validated via Fe²⁺-loaded liposomal models at the single-particle level. Comprehensive profiling of Fe²⁺ distributions in HT-1080-derived EVs under varying ferroptotic stress conditions revealed the striking heterogeneity in Fe²⁺ loading among EVs and a strong positive correlation between EV Fe²⁺ levels and their parental cells. Notably, we identified an EV-mediated Fe²⁺ export mechanism that functionally parallels to ferroportin (FPN)-dependent iron efflux, suggesting EVs may serve as a compensatory iron-release pathway during FPN inhibition. The nFCM platform achieved superior detection sensitivity with high throughput (up to 10⁴ particles per min), providing a powerful analytical tool for investigating EV heterogeneity and Fe²⁺-mediated regulatory networks in iron homeostasis and ferroptosis-related pathologies.

Single extracellular vesicles (EVs) carry molecular signatures from their cell of origin, making them a pivotal non-invasive biomarker for cancer diagnosis and monitoring. However, analyzing the complex data associated with single-EVs, such as fingerprints generated via Surface-enhanced Raman Spectroscopy (SERS), remains challenging. To address this, a thorough comparison of machine learning models' implementations and their accuracy classification optimization is presented. A comprehensive single-EV spectral library collected with a SERS-assisted nanostructured platform including cell lines, healthy controls, and cancer patient samples is used. The performance of different learning models (random forests, support vector machines, convolutional neural networks, and linear regression as reference) was assessed for cancer detection tasks: i) multi-cell line classification and ii) cancerous versus non-cancerous binary classification. To improve their accuracy, we optimized spectra preprocessing, artificially increased the dataset, and implemented feature-driven classification. In sum, these methods enabled more interpretable models to perform on par with the complex one, increasing accuracy up to 12% percent-age points, even with datasets reduced to 66% of the original size. Achieving accuracies of 83% and 91% for Task-i and Task-ii, respectively.